Methods and apparatus to determine impressions using distributed demographic information

ABSTRACT

Methods and apparatus to determine media impressions using distributed demographic information are disclosed. An example method to monitor media exposure involves receiving a first request from a client device at an intermediary serving a first sub-domain of a first internet domain, the first request to be indicative of access to media at the client device; transmitting, from the intermediary, first data associated with the first request to a data collection server of an audience measurement entity and second data about the first request to an entity of the first internet domain; and in response to determining that the first request does not include a user identifier associated with the first internet domain, sending a first response from the intermediary to the client device, the first response to instruct the client device to send a second request to a second intermediary associated with a second sub-domain of a second internet domain, the second request indicative of the access to the media at the client device.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 14/192,852, filed on Feb. 27, 2014, which is a continuation of U.S.patent application Ser. No. 13/239,005, filed on Sep. 21, 2011, now U.S.Pat. No. 8,713,168, which claims priority to U.S. Provisional PatentApplication No. 61/385,553, filed on Sep. 22, 2010, and U.S. ProvisionalPatent Application No. 61/386,543, filed on Sep. 26, 2010. U.S. patentapplication Ser. No. 14/192,852, U.S. patent application Ser. No.13/239,005, U.S. Provisional Patent Application No. 61/385,553, and U.S.Provisional Patent Application No. 61/386,543 are hereby incorporatedherein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to monitoring media and, moreparticularly, to methods and apparatus to determine impressions usingdistributed demographic information.

BACKGROUND

Traditionally, audience measurement entities determine audienceengagement levels for media programming based on registered panelmembers. That is, an audience measurement entity enrolls people whoconsent to being monitored into a panel. The audience measurement entitythen monitors those panel members to determine media programs (e.g.,television programs or radio programs, movies, DVDs, etc.) exposed tothose panel members. In this manner, the audience measurement entity candetermine exposure measures for different media content based on thecollected media measurement data.

Techniques for monitoring user access to Internet resources such as webpages, advertisements and/or other content has evolved significantlyover the years. Some known systems perform such monitoring primarilythrough server logs. In particular, entities serving content on theInternet can use known techniques to log the number of requests receivedfor their content at their server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example system that may be used to determineadvertisement viewership using distributed demographic information.

FIG. 2 depicts an example system that may be used to associateadvertisement exposure measurements with user demographic informationbased on demographics information distributed across user accountrecords of different web service providers.

FIG. 3 is a communication flow diagram of an example manner in which aweb browser can report impressions to servers having access todemographic information for a user of that web browser.

FIG. 4 depicts an example ratings entity impressions table showingquantities of impressions to monitored users.

FIG. 5 depicts an example campaign-level age/gender and impressioncomposition table generated by a database proprietor.

FIG. 6 depicts another example campaign-level age/gender and impressioncomposition table generated by a ratings entity.

FIG. 7 depicts an example combined campaign-level age/gender andimpression composition table based on the composition tables of FIGS. 5and 6.

FIG. 8 depicts an example age/gender impressions distribution tableshowing impressions based on the composition tables of FIGS. 5-7.

FIG. 9 is a flow diagram representative of example machine readableinstructions that may be executed to identify demographics attributableto impressions.

FIG. 10 is a flow diagram representative of example machine readableinstructions that may be executed by a client computer to route beaconrequests to web service providers to log impressions.

FIG. 11 is a flow diagram representative of example machine readableinstructions that may be executed by a panelist monitoring system to logimpressions and/or redirect beacon requests to web service providers tolog impressions.

FIG. 12 is a flow diagram representative of example machine readableinstructions that may be executed to dynamically designate preferred webservice providers from which to request demographics attributable toimpressions.

FIG. 13 depicts an example system that may be used to determineadvertising exposure based on demographic information collected by oneor more database proprietors.

FIG. 14 is a flow diagram representative of example machine readableinstructions that may be executed to process a redirected request at anintermediary.

FIG. 15 is an example processor system that can be used to execute theexample instructions of FIGS. 9, 10, 11, 12, and/or 14 to implement theexample apparatus and systems described herein.

DETAILED DESCRIPTION

Although the following discloses example methods, apparatus, systems,and articles of manufacture including, among other components, firmwareand/or software executed on hardware, it should be noted that suchmethods, apparatus, systems, and articles of manufacture are merelyillustrative and should not be considered as limiting. For example, itis contemplated that any or all of these hardware, firmware, and/orsoftware components could be embodied exclusively in hardware,exclusively in firmware, exclusively in software, or in any combinationof hardware, firmware, and/or software. Accordingly, while the followingdescribes example methods, apparatus, systems, and articles ofmanufacture, the examples provided are not the only ways to implementsuch methods, apparatus, systems, and articles of manufacture.

Techniques for monitoring user access to Internet resources such as webpages, advertisements and/or other content has evolved significantlyover the years. At one point in the past, such monitoring was doneprimarily through server logs. In particular, entities serving contenton the Internet would log the number of requests received for theircontent at their server. Basing Internet usage research on server logsis problematic for several reasons. For example, server logs can betampered with either directly or via zombie programs which repeatedlyrequest content from the server to increase the server log counts.Secondly, content is sometimes retrieved once, cached locally and thenrepeatedly viewed from the local cache without involving the server inthe repeat viewings. Server logs cannot track these views of cachedcontent. Thus, server logs are susceptible to both over-counting andunder-counting errors.

The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637,fundamentally changed the way Internet monitoring is performed andovercame the limitations of the server side log monitoring techniquesdescribed above. For example, Blumenau disclosed a technique whereinInternet content to be tracked is tagged with beacon instructions. Inparticular, monitoring instructions are associated with the HTML of thecontent to be tracked. When a client requests the content, both thecontent and the beacon instructions are downloaded to the client. Thebeacon instructions are, thus, executed whenever the content isaccessed, be it from a server or from a cache.

The beacon instructions cause monitoring data reflecting informationabout the access to the content to be sent from the client thatdownloaded the content to a monitoring entity. Typically, the monitoringentity is an audience measurement entity that did not provide thecontent to the client and who is a trusted third party for providingaccurate usage statistics (e.g., The Nielsen Company, LLC).Advantageously, because the beaconing instructions are associated withthe content and executed by the client browser whenever the content isaccessed, the monitoring information is provided to the audiencemeasurement company irrespective of whether the client is a panelist ofthe audience measurement company.

It is important, however, to link demographics to the monitoringinformation. To address this issue, the audience measurement companyestablishes a panel of users who have agreed to provide theirdemographic information and to have their Internet browsing activitiesmonitored. When an individual joins the panel, they provide detailedinformation concerning their identity and demographics (e.g., gender,race, income, home location, occupation, etc.) to the audiencemeasurement company. The audience measurement entity sets a cookie onthe panelist computer that enables the audience measurement entity toidentify the panelist whenever the panelist accesses tagged content and,thus, sends monitoring information to the audience measurement entity.

Since most of the clients providing monitoring information from thetagged pages are not panelists and, thus, are unknown to the audiencemeasurement entity, it is necessary to use statistical methods to imputedemographic information based on the data collected for panelists to thelarger population of users providing data for the tagged content.However, panel sizes of audience measurement entities remain smallcompared to the general population of users. Thus, a problem ispresented as to how to increase panel sizes while ensuring thedemographics data of the panel is accurate.

There are many database proprietors operating on the Internet. Thesedatabase proprietors provide services to large numbers of subscribers.In exchange for the provision of the service, the subscribers registerwith the proprietor. As part of this registration, the subscribersprovide detailed demographic information. Examples of such databaseproprietors include social network providers such as Facebook, Myspace,etc. These database proprietors set cookies on the computers of theirsubscribers to enable the database proprietor to recognize the user whenthey visit their website.

The protocols of the Internet make cookies inaccessible outside of thedomain (e.g., Internet domain, domain name, etc.) on which they wereset. Thus, a cookie set in the amazon.com domain is accessible toservers in the amazon.com domain, but not to servers outside thatdomain. Therefore, although an audience measurement entity might find itadvantageous to access the cookies set by the database proprietors, theyare unable to do so.

In view of the foregoing, an audience measurement company would like toleverage the existing databases of database proprietors to collect moreextensive Internet usage and demographic data. However, the audiencemeasurement entity is faced with several problems in accomplishing thisend. For example, a problem is presented as to how to access the data ofthe database proprietors without compromising the privacy of thesubscribers, the panelists, or the proprietors of the tracked content.Another problem is how to access this data given the technicalrestrictions imposed by the Internet protocols that prevent the audiencemeasurement entity from accessing cookies set by the databaseproprietor. Example methods, apparatus and articles of manufacturedisclosed herein solve these problems by extending the beaconing processto encompass partnered database proprietors and by using such partnersas interim data collectors.

Example methods, apparatus and/or articles of manufacture disclosedherein accomplish this task by responding to beacon requests fromclients (who may not be a member of an audience member panel and, thus,may be unknown to the audience member entity) accessing tagged contentby redirecting the client from the audience measurement entity to adatabase proprietor such as a social network site partnered with theaudience member entity. The redirection initiates a communicationsession between the client accessing the tagged content and the databaseproprietor. The database proprietor (e.g., Facebook) can access anycookie it has set on the client to thereby identify the client based onthe internal records of the database proprietor. In the event the clientis a subscriber of the database proprietor, the database proprietor logsthe content impression in association with the demographics data of theclient and subsequently forwards the log to the audience measurementcompany. In the event the client is not a subscriber of the databaseproprietor, the database proprietor redirects the client to the audiencemeasurement company. The audience measurement company may then redirectthe client to a second, different database proprietor that is partneredwith the audience measurement entity. That second proprietor may thenattempt to identify the client as explained above. This process ofredirecting the client from database proprietor to database proprietorcan be performed any number of times until the client is identified andthe content exposure logged, or until all partners have been contactedwithout a successful identification of the client. The redirections alloccur automatically so the user of the client is not involved in thevarious communication sessions and may not even know they are occurring.

The partnered database proprietors provide their logs and demographicinformation to the audience measurement entity which then compiles thecollected data into statistical reports accurately identifying thedemographics of persons accessing the tagged content. Because theidentification of clients is done with reference to enormous databasesof users far beyond the quantity of persons present in a conventionalaudience measurement panel, the data developed from this process isextremely accurate, reliable and detailed.

Significantly, because the audience measurement entity remains the firstleg of the data collection process (e.g., receives the request generatedby the beacon instructions from the client), the audience measuremententity is able to obscure the source of the content access being loggedas well as the identity of the content itself from the databaseproprietors (thereby protecting the privacy of the content sources),without compromising the ability of the database proprietors to logimpressions for their subscribers. Further, the Internet security cookieprotocols are complied with because the only servers that access a givencookie are associated with the Internet domain (e.g., Facebook.com) thatset that cookie.

Example methods, apparatus, and articles of manufacture described hereincan be used to determine content impressions, advertisement impressions,content exposure, and/or advertisement exposure using demographicinformation, which is distributed across different databases (e.g.,different website owners, service providers, etc.) on the Internet. Notonly do example methods, apparatus, and articles of manufacturedisclosed herein enable more accurate correlation of Internetadvertisement exposure to demographics, but they also effectively extendpanel sizes and compositions beyond persons participating in the panelof an audience measurement entity and/or a ratings entity to personsregistered in other Internet databases such as the databases of socialmedium sites such as Facebook, Twitter, Google, etc. This extensioneffectively leverages the content tagging capabilities of the ratingsentity and the use of databases of non-ratings entities such as socialmedia and other websites to create an enormous, demographically accuratepanel that results in accurate, reliable measurements of exposures toInternet content such as advertising and/or programming.

In illustrated examples disclosed herein, advertisement exposure ismeasured in terms of online Gross Rating Points. A Gross Rating Point(GRP) is a unit of measurement of audience size that has traditionallybeen used in the television ratings context. It is used to measureexposure to one or more programs, advertisements, or commercials,without regard to multiple exposures of the same advertising toindividuals. In terms of television (TV) advertisements, one GRP isequal to 1% of TV households. While GRPs have traditionally been used asa measure of television viewership, example methods, apparatus, andarticles of manufacture disclosed herein develop online GRPs for onlineadvertising to provide a standardized metric that can be used across theInternet to accurately reflect online advertisement exposure. Suchstandardized online GRP measurements can provide greater certainty toadvertisers that their online advertisement money is well spent. It canalso facilitate cross-medium comparisons such as viewership of TVadvertisements and online advertisements. Because the example methods,apparatus, and/or articles of manufacture disclosed herein associateviewership measurements with corresponding demographics of users, theinformation collected by example methods, apparatus, and/or articles ofmanufacture disclosed herein may also be used by advertisers to identifymarkets reached by their advertisements and/or to target particularmarkets with future advertisements.

Traditionally, audience measurement entities (also referred to herein as“ratings entities”) determine demographic reach for advertising andmedia programming based on registered panel members. That is, anaudience measurement entity enrolls people that consent to beingmonitored into a panel. During enrollment, the audience measuremententity receives demographic information from the enrolling people sothat subsequent correlations may be made between advertisement/mediaexposure to those panelists and different demographic markets. Unliketraditional techniques in which audience measurement entities relysolely on their own panel member data to collect demographics-basedaudience measurement, example methods, apparatus, and/or articles ofmanufacture disclosed herein enable an audience measurement entity toshare demographic information with other entities that operate based onuser registration models. As used herein, a user registration model is amodel in which users subscribe to services of those entities by creatingan account and providing demographic-related information aboutthemselves. Sharing of demographic information associated withregistered users of database proprietors enables an audience measuremententity to extend or supplement their panel data with substantiallyreliable demographics information from external sources (e.g., databaseproprietors), thus extending the coverage, accuracy, and/or completenessof their demographics-based audience measurements. Such access alsoenables the audience measurement entity to monitor persons who would nototherwise have joined an audience measurement panel. Any entity having adatabase identifying demographics of a set of individuals may cooperatewith the audience measurement entity. Such entities may be referred toas “database proprietors” and include entities such as Facebook, Google,Yahoo!, MSN, Twitter, Apple iTunes, Experian, etc.

Example methods, apparatus, and/or articles of manufacture disclosedherein may be implemented by an audience measurement entity (e.g., anyentity interested in measuring or tracking audience exposures toadvertisements, content, and/or any other media) in cooperation with anynumber of database proprietors such as online web services providers todevelop online GRPs. Such database proprietors/online web servicesproviders may be social network sites (e.g., Facebook, Twitter, MySpace,etc.), multi-service sites (e.g., Yahoo!, Google, Experian, etc.),online retailer sites (e.g., Amazon.com, Buy.com, etc.), and/or anyother web service(s) site that maintains user registration records.

To increase the likelihood that measured viewership is accuratelyattributed to the correct demographics, example methods, apparatus,and/or articles of manufacture disclosed herein use demographicinformation located in the audience measurement entity's records as wellas demographic information located at one or more database proprietors(e.g., web service providers) that maintain records or profiles of usershaving accounts therewith. In this manner, example methods, apparatus,and/or articles of manufacture disclosed herein may be used tosupplement demographic information maintained by a ratings entity (e.g.,an audience measurement company such as The Nielsen Company ofSchaumburg, Ill., United States of America, that collects media exposuremeasurements and/or demographics) with demographic information from oneor more different database proprietors (e.g., web service providers).

The use of demographic information from disparate data sources (e.g.,high-quality demographic information from the panels of an audiencemeasurement company and/or registered user data of web serviceproviders) results in improved reporting effectiveness of metrics forboth online and offline advertising campaigns. Example techniquesdisclosed herein use online registration data to identify demographicsof users and use server impression counts, tagging (also referred to asbeaconing), and/or other techniques to track quantities of impressionsattributable to those users. Online web service providers such as socialnetworking sites (e.g., Facebook) and multi-service providers (e.g.,Yahoo!, Google, Experian, etc.) (collectively and individually referredto herein as online database proprietors) maintain detailed demographicinformation (e.g., age, gender, geographic location, race, income level,education level, religion, etc.) collected via user registrationprocesses. An impression corresponds to a home or individual having beenexposed to the corresponding media content and/or advertisement. Thus,an impression represents a home or an individual having been exposed toan advertisement or content or group of advertisements or content. InInternet advertising, a quantity of impressions or impression count isthe total number of times an advertisement or advertisement campaign hasbeen accessed by a web population (e.g., including number of timesaccessed as decreased by, for example, pop-up blockers and/or increasedby, for example, retrieval from local cache memory).

Example methods, apparatus, and/or articles of manufacture disclosedherein also enable reporting TV GRPs and online GRPs in a side-by-sidemanner. For instance, techniques disclosed herein enable advertisers toreport quantities of unique people or users that are reachedindividually and/or collectively by TV and/or online advertisements.

Example methods, apparatus, and/or articles of manufacture disclosedherein also collect impressions mapped to demographics data at variouslocations on the Internet. For example, an audience measurement entitycollects such impression data for its panel and automatically enlistsone or more online demographics proprietors to collect impression datafor their subscribers. By combining this collected impression data, theaudience measurement entity can then generate GRP metrics for differentadvertisement campaigns. These GRP metrics can be correlated orotherwise associated with particular demographic segments and/or marketsthat were reached.

FIG. 1 depicts an example system 100 that may be used to determine mediaexposure (e.g., exposure to content and/or advertisements) based ondemographic information collected by one or more database proprietors.“Distributed demographics information” is used herein to refer todemographics information obtained from at least two sources, at leastone of which is a database proprietor such as an online web servicesprovider. In the illustrated example, content providers and/oradvertisers distribute advertisements 102 via the Internet 104 to usersthat access websites and/or online television services (e.g., web-basedTV, Internet protocol TV (IPTV), etc.). The advertisements 102 mayadditionally or alternatively be distributed through broadcasttelevision services to traditional non-Internet based (e.g., RF,terrestrial or satellite based) television sets and monitored forviewership using the techniques described herein and/or othertechniques. Websites, movies, television and/or other programming isgenerally referred to herein as content. Advertisements are typicallydistributed with content. Traditionally, content is provided at littleor no cost to the audience because it is subsidized by advertisers whypay to have their advertisements distributed with the content.

In the illustrated example, the advertisements 102 may form one or moread campaigns and are encoded with identification codes (e.g., metadata)that identify the associated ad campaign (e.g., campaign ID), a creativetype ID (e.g., identifying a Flash-based ad, a banner ad, a rich typead, etc.), a source ID (e.g., identifying the ad publisher), and aplacement ID (e.g., identifying the physical placement of the ad on ascreen). The advertisements 102 are also tagged or encoded to includecomputer executable beacon instructions (e.g., Java, javascript, or anyother computer language or script) that are executed by web browsersthat access the advertisements 102 on, for example, the Internet.Computer executable beacon instructions may additionally oralternatively be associated with content to be monitored. Thus, althoughthis disclosure frequently speaks in the area of trackingadvertisements, it is not restricted to tracking any particular type ofmedia. On the contrary, it can be used to track content oradvertisements of any type or form in a network. Irrespective of thetype of content being tracked, execution of the beacon instructionscauses the web browser to send an impression request (e.g., referred toherein as beacon requests) to a specified server (e.g., the audiencemeasurement entity). The beacon request may be implemented as an HTTPrequest. However, whereas a transmitted HTML request identifies awebpage or other resource to be downloaded, the beacon request includesthe audience measurement information (e.g., ad campaign identification,content identifier, and/or user identification information) as itspayload. The server to which the beacon request is directed isprogrammed to log the audience measurement data of the beacon request asan impression (e.g., an ad and/or content impressions depending on thenature of the media tagged with the beaconing instruction).

In some example implementations, advertisements tagged with such beaconinstructions may be distributed with Internet-based media contentincluding, for example, web pages, streaming video, streaming audio,IPTV content, etc. and used to collect demographics-based impressiondata. As noted above, methods, apparatus, and/or articles of manufacturedisclosed herein are not limited to advertisement monitoring but can beadapted to any type of content monitoring (e.g., web pages, movies,television programs, etc.). Example techniques that may be used toimplement such beacon instructions are disclosed in Blumenau, U.S. Pat.No. 6,108,637, which is hereby incorporated herein by reference in itsentirety.

Although example methods, apparatus, and/or articles of manufacture aredescribed herein as using beacon instructions executed by web browsersto send beacon requests to specified impression collection servers, theexample methods, apparatus, and/or articles of manufacture mayadditionally collect data with on-device meter systems that locallycollect web browsing information without relying on content oradvertisements encoded or tagged with beacon instructions. In suchexamples, locally collected web browsing behavior may subsequently becorrelated with user demographic data based on user IDs as disclosedherein.

The example system 100 of FIG. 1 includes a ratings entity subsystem106, a partner database proprietor subsystem 108 (implemented in thisexample by a social network service provider), other partnered databaseproprietor (e.g., web service provider) subsystems 110, andnon-partnered database proprietor (e.g., web service provider)subsystems 112. In the illustrated example, the ratings entity subsystem106 and the partnered database proprietor subsystems 108, 110 correspondto partnered business entities that have agreed to share demographicinformation and to capture impressions in response to redirected beaconrequests as explained below. The partnered business entities mayparticipate to advantageously have the accuracy and/or completeness oftheir respective demographic information confirmed and/or increased. Thepartnered business entities also participate in reporting impressionsthat occurred on their websites. In the illustrated example, the otherpartnered database proprietor subsystems 110 include components,software, hardware, and/or processes similar or identical to thepartnered database proprietor subsystem 108 to collect and logimpressions (e.g., advertisement and/or content impressions) andassociate demographic information with such logged impressions.

The non-partnered database proprietor subsystems 112 correspond tobusiness entities that do not participate in sharing of demographicinformation. However, the techniques disclosed herein do trackimpressions (e.g., advertising impressions and/or content impressions)attributable to the non-partnered database proprietor subsystems 112,and in some instances, one or more of the non-partnered databaseproprietor subsystems 112 also report unique user IDs (UUIDs)attributable to different impressions. Unique user IDs can be used toidentify demographics using demographics information maintained by thepartnered business entities (e.g., the ratings entity subsystem 106and/or the database proprietor subsystems 108, 110).

The database proprietor subsystem 108 of the example of FIG. 1 isimplemented by a social network proprietor such as Facebook. However,the database proprietor subsystem 108 may instead be operated by anyother type of entity such as a web services entity that servesdesktop/stationary computer users and/or mobile device users. In theillustrated example, the database proprietor subsystem 108 is in a firstinternet domain, and the partnered database proprietor subsystems 110and/or the non-partnered database proprietor subsystems 112 are insecond, third, fourth, etc. internet domains.

In the illustrated example of FIG. 1, the tracked content and/oradvertisements 102 are presented to TV and/or PC (computer) panelists114 and online only panelists 116. The panelists 114 and 116 are usersregistered on panels maintained by a ratings entity (e.g., an audiencemeasurement company) that owns and/or operates the ratings entitysubsystem 106. In the example of FIG. 1, the TV and PC panelists 114include users and/or homes that are monitored for exposures to thecontent and/or advertisements 102 on TVs and/or computers. The onlineonly panelists 116 include users that are monitored for exposure (e.g.,content exposure and/or advertisement exposure) via online sources whenat work or home. In some example implementations, TV and/or PC panelists114 may be home-centric users (e.g., home-makers, students, adolescents,children, etc.), while online only panelists 116 may be business-centricusers that are commonly connected to work-provided Internet services viaoffice computers or mobile devices (e.g., mobile phones, smartphones,laptops, tablet computers, etc.).

To collect exposure measurements (e.g., content impressions and/oradvertisement impressions) generated by meters at client devices (e.g.,computers, mobile phones, smartphones, laptops, tablet computers, TVs,etc.), the ratings entity subsystem 106 includes a ratings entitycollector 117 and loader 118 to perform collection and loadingprocesses. The ratings entity collector 117 and loader 118 collect andstore the collected exposure measurements obtained via the panelists 114and 116 in a ratings entity database 120. The ratings entity subsystem106 then processes and filters the exposure measurements based onbusiness rules 122 and organizes the processed exposure measurementsinto TV&PC summary tables 124, online home (H) summary tables 126, andonline work (W) summary tables 128. In the illustrated example, thesummary tables 124, 126, and 128 are sent to a GRP report generator 130,which generates one or more GRP report(s) 131 to sell or otherwiseprovide to advertisers, publishers, manufacturers, content providers,and/or any other entity interested in such market research.

In the illustrated example of FIG. 1, the ratings entity subsystem 106is provided with an impression monitor system 132 that is configured totrack exposure quantities (e.g., content impressions and/oradvertisement impressions) corresponding to content and/oradvertisements presented by client devices (e.g., computers, mobilephones, smartphones, laptops, tablet computers, etc.) whether receivedfrom remote web servers or retrieved from local caches of the clientdevices. In some example implementations, the impression monitor system132 may be implemented using the SiteCensus system owned and operated byThe Nielsen Company. In the illustrated example, identities of usersassociated with the exposure quantities are collected using cookies(e.g., Universally Unique Identifiers (UUIDs)) tracked by the impressionmonitor system 132 when client devices present content and/oradvertisements. Due to Internet security protocols, the impressionmonitor system 132 can only collect cookies set in its domain. Thus, if,for example, the impression monitor system 132 operates in the“Nielsen.com” domain, it can only collect cookies set by a Nielsen.comserver. Thus, when the impression monitor system 132 receives a beaconrequest from a given client, the impression monitor system 132 only hasaccess to cookies set on that client by a server in the, for example,Nielsen.com domain. To overcome this limitation, the impression monitorsystem 132 of the illustrated example is structured to forward beaconrequests to one or more database proprietors partnered with the audiencemeasurement entity. Those one or more partners can recognize cookies setin their domain (e.g., Facebook.com) and therefore log impressions inassociation with the subscribers associated with the recognized cookies.This process is explained further below.

In the illustrated example, the ratings entity subsystem 106 includes aratings entity cookie collector 134 to collect cookie information (e.g.,user ID information) together with content IDs and/or ad IDs associatedwith the cookies from the impression monitor system 132 and send thecollected information to the GRP report generator 130. Again, thecookies collected by the impression monitor system 132 are those set byserver(s) operating in a domain of the audience measurement entity. Insome examples, the ratings entity cookie collector 134 is configured tocollect logged impressions (e.g., based on cookie information and ad orcontent IDs) from the impression monitor system 132 and provide thelogged impressions to the GRP report generator 130.

The operation of the impression monitor system 132 in connection withclient devices and partner sites is described below in connection withFIGS. 2 and 3. In particular, FIGS. 2 and 3 depict how the impressionmonitor system 132 enables collecting user identities and trackingexposure quantities for content and/or advertisements exposed to thoseusers. The collected data can be used to determine information about,for example, the effectiveness of advertisement campaigns.

For purposes of example, the following example involves a social networkprovider, such as Facebook, as the database proprietor. In theillustrated example, the database proprietor subsystem 108 includesservers 138 to store user registration information, perform web serverprocesses to serve web pages (possibly, but not necessarily includingone or more advertisements) to subscribers of the social network, totrack user activity, and to track account characteristics. Duringaccount creation, the database proprietor subsystem 108 asks users toprovide demographic information such as age, gender, geographiclocation, graduation year, quantity of group associations, and/or anyother personal or demographic information. To automatically identifyusers on return visits to the webpage(s) of the social network entity,the servers 138 set cookies on client devices (e.g., computers and/ormobile devices of registered users, some of which may be panelists 114and 116 of the audience measurement entity and/or may not be panelistsof the audience measurement entity). The cookies may be used to identifyusers to track user visits to the webpages of the social network entity,to display those web pages according to the preferences of the users,etc. The cookies set by the database proprietor subsystem 108 may alsobe used to collect “domain specific” user activity. As used herein,“domain specific” user activity is user Internet activity occurringwithin the domain(s) of a single entity. Domain specific user activitymay also be referred to as “intra-domain activity.” The social networkentity may collect intra-domain activity such as the number of web pages(e.g., web pages of the social network domain such as other socialnetwork member pages or other intra-domain pages) visited by eachregistered user and/or the types of devices such as mobile (e.g.,smartphones) or stationary (e.g., desktop computers) devices used forsuch access. The servers 138 are also configured to track accountcharacteristics such as the quantity of social connections (e.g.,friends) maintained by each registered user, the quantity of picturesposted by each registered user, the quantity of messages sent orreceived by each registered user, and/or any other characteristic ofuser accounts.

The database proprietor subsystem 108 includes a database proprietor(DP) collector 139 and a DP loader 140 to collect user registration data(e.g., demographic data), intra-domain user activity data, inter-domainuser activity data (as explained later) and account characteristicsdata. The collected information is stored in a database proprietordatabase 142. The database proprietor subsystem 108 processes thecollected data using business rules 144 to create DP summary tables 146.

In the illustrated example, the other partnered database proprietorsubsystems 110 may share with the audience measurement entity similartypes of information as that shared by the database proprietor subsystem108. In this manner, demographic information of people that are notregistered users of the social network services provider may be obtainedfrom one or more of the other partnered database proprietor subsystems110 if they are registered users of those web service providers (e.g.,Yahoo!, Google, Experian, etc.). Example methods, apparatus, and/orarticles of manufacture disclosed herein advantageously use thiscooperation or sharing of demographic information across website domainsto increase the accuracy and/or completeness of demographic informationavailable to the audience measurement entity. By using the shareddemographic data in such a combined manner with information identifyingthe content and/or ads 102 to which users are exposed, example methods,apparatus, and/or articles of manufacture disclosed herein produce moreaccurate exposure-per-demographic results to enable a determination ofmeaningful and consistent GRPs for online advertisements.

As the system 100 expands, more partnered participants (e.g., like thepartnered database proprietor subsystems 110) may join to share furtherdistributed demographic information and advertisement viewershipinformation for generating GRPs.

To preserve user privacy, the example methods, apparatus, and/orarticles of manufacture described herein use double encryptiontechniques by each participating partner or entity (e.g., the subsystems106, 108, 110) so that user identities are not revealed when sharingdemographic and/or viewership information between the participatingpartners or entities. In this manner, user privacy is not compromised bythe sharing of the demographic information as the entity receiving thedemographic information is unable to identify the individual associatedwith the received demographic information unless those individuals havealready consented to allow access to their information by, for example,previously joining a panel or services of the receiving entity (e.g.,the audience measurement entity). If the individual is already in thereceiving party's database, the receiving party will be able to identifythe individual despite the encryption. However, the individual hasalready agreed to be in the receiving party's database, so consent toallow access to their demographic and behavioral information haspreviously already been received.

FIG. 2 depicts an example system 200 that may be used to associateexposure measurements with user demographic information based ondemographics information distributed across user account records ofdifferent database proprietors (e.g., web service providers). Theexample system 200 enables the ratings entity subsystem 106 of FIG. 1 tolocate a best-fit partner (e.g., the database proprietor subsystem 108of FIG. 1 and/or one of the other partnered database proprietorsubsystems 110 of FIG. 1) for each beacon request (e.g., a request froma client executing a tag associated with tagged media such as anadvertisement or content that contains data identifying the media toenable an entity to log an exposure or impression). In some examples,the example system 200 uses rules and machine learning classifiers(e.g., based on an evolving set of empirical data) to determine arelatively best-suited partner that is likely to have demographicsinformation for a user that triggered a beacon request. The rules may beapplied based on a publisher level, a campaign/publisher level, or auser level. In some examples, machine learning is not employed andinstead, the partners are contacted in some ordered fashion (e.g.,Facebook, Myspace, then Yahoo!, etc.) until the user associated with abeacon request is identified or all partners are exhausted without anidentification.

The ratings entity subsystem 106 receives and compiles the impressiondata from all available partners. The ratings entity subsystem 106 mayweight the impression data based on the overall reach and demographicquality of the partner sourcing the data. For example, the ratingsentity subsystem 106 may refer to historical data on the accuracy of apartners demographic data to assign a weight to the logged data providedby that partner.

For rules applied at a publisher level, a set of rules and classifiersare defined that allow the ratings entity subsystem 106 to target themost appropriate partner for a particular publisher (e.g., a publisherof one or more of the advertisements or content 102 of FIG. 1). Forexample, the ratings entity subsystem 106 could use the demographiccomposition of the publisher and partner web service providers to selectthe partner most likely to have an appropriate user base (e.g.,registered users that are likely to access content for the correspondingpublisher).

For rules applied at a campaign level, for instances in which apublisher has the ability to target an ad campaign based on userdemographics, the target partner site could be defined at thepublisher/campaign level. For example, if an ad campaign is targeted atmales aged between the ages of 18 and 25, the ratings entity subsystem106 could use this information to direct a request to the partner mostlikely to have the largest reach within that gender/age group (e.g., adatabase proprietor that maintains a sports website, etc.).

For rules applied at the user level (or cookie level), the ratingsentity subsystem 106 can dynamically select a preferred partner toidentify the client and log the impression based on, for example, (1)feedback received from partners (e.g., feedback indicating that panelistuser IDs did not match registered users of the partner site orindicating that the partner site does not have a sufficient number ofregistered users), and/or (2) user behavior (e.g., user browsingbehavior may indicate that certain users are unlikely to have registeredaccounts with particular partner sites). In the illustrated example ofFIG. 2, rules may be used to specify when to override a user levelpreferred partner with a publisher (or publisher campaign) level partnertarget.

Turning in detail to FIG. 2, a panelist computer 202 represents acomputer used by one or more of the panelists 114 and 116 of FIG. 1. Asshown in the example of FIG. 2, the panelist computer 202 may exchangecommunications with the impression monitor system 132 of FIG. 1. In theillustrated example, a partner A 206 may be the database proprietorsubsystem 108 of FIG. 1 and a partner B 208 may be one of the otherpartnered database proprietor subsystems 110 of FIG. 1. A panelcollection platform 210 contains the ratings entity database 120 of FIG.1 to collect ad and/or content exposure data (e.g., impression data orcontent impression data). Interim collection platforms are likelylocated at the partner A 206 and partner B 208 sites to store loggedimpressions, at least until the data is transferred to the audiencemeasurement entity.

The panelist computer 202 of the illustrated example executes a webbrowser 212 that is directed to a host website (e.g., www.acme.com) thatdisplays one of the advertisements and/or content 102. The advertisementand/or content 102 is tagged with identifier information (e.g., acampaign ID, a creative type ID, a placement ID, a publisher source URL,etc.) and beacon instructions 214. When the beacon instructions 214 areexecuted by the panelist computer 202, the beacon instructions cause thepanelist computer to send a beacon request to a remote server specifiedin the beacon instructions 214. In the illustrated example, thespecified server is a server of the audience measurement entity, namely,at the impression monitor system 132. The beacon instructions 214 may beimplemented using javascript or any other types of instructions orscript executable via a web browser including, for example, Java, HTML,etc. It should be noted that tagged webpages and/or advertisements areprocessed the same way by panelist and non-panelist computers. In bothsystems, the beacon instructions are received in connection with thedownload of the tagged content and cause a beacon request to be sentfrom the client that downloaded the tagged content for the audiencemeasurement entity. A non-panelist computer is shown at reference number203. Although the client 203 is not a panelist 114, 116, the impressionmonitor system 132 may interact with the client 203 in the same manneras the impression monitor system 132 interacts with the client computer202, associated with one of the panelists 114, 116. As shown in FIG. 2,the non-panelist client 203 also sends a beacon request 215 based ontagged content downloaded and presented on the non-panelist client 203.As a result, in the following description panelist computer 202 andnon-panelist computer 203 are referred to generically as a “client”computer.

In the illustrated example, the web browser 212 stores one or morepartner cookie(s) 216 and a panelist monitor cookie 218. Each partnercookie 216 corresponds to a respective partner (e.g., the partners A 206and B 208) and can be used only by the respective partner to identify auser of the panelist computer 202. The panelist monitor cookie 218 is acookie set by the impression monitor system 132 and identifies the userof the panelist computer 202 to the impression monitor system 132. Eachof the partner cookies 216 is created, set, or otherwise initialized inthe panelist computer 202 when a user of the computer first visits awebsite of a corresponding partner (e.g., one of the partners A 206 andB 208) and/or when a user of the computer registers with the partner(e.g., sets up a Facebook account). If the user has a registered accountwith the corresponding partner, the user ID (e.g., an email address orother value) of the user is mapped to the corresponding partner cookie216 in the records of the corresponding partner. The panelist monitorcookie 218 is created when the client (e.g., a panelist computer or anon-panelist computer) registers for the panel and/or when the clientprocesses a tagged advertisement. The panelist monitor cookie 218 of thepanelist computer 202 may be set when the user registers as a panelistand is mapped to a user ID (e.g., an email address or other value) ofthe user in the records of the ratings entity. Although the non-panelistclient computer 203 is not part of a panel, a panelist monitor cookiesimilar to the panelist monitor cookie 218 is created in thenon-panelist client computer 203 when the non-panelist client computer203 processes a tagged advertisement. In this manner, the impressionmonitor system 132 may collect impressions (e.g., ad impressions)associated with the non-panelist client computer 203 even though a userof the non-panelist client computer 203 is not registered in a panel andthe ratings entity operating the impression monitor system 132 will nothave demographics for the user of the non-panelist client computer 203.

In some examples, the web browser 212 may also include apartner-priority-order cookie 220 that is set, adjusted, and/orcontrolled by the impression monitor system 132 and includes a prioritylisting of the partners 206 and 208 (and/or other database proprietors)indicative of an order in which beacon requests should be sent to thepartners 206, 208 and/or other database proprietors. For example, theimpression monitor system 132 may specify that the client computer 202,203 should first send a beacon request based on execution of the beaconinstructions 214 to partner A 206 and then to partner B 208 if partner A206 indicates that the user of the client computer 202, 203 is not aregistered user of partner A 206. In this manner, the client computer202, 203 can use the beacon instructions 214 in combination with thepriority listing of the partner-priority-order cookie 220 to send aninitial beacon request to an initial partner and/or other initialdatabase proprietor and one or more redirected beacon requests to one ormore secondary partners and/or other database proprietors until one ofthe partners 206 and 208 and/or other database proprietors confirms thatthe user of the panelist computer 202 is a registered user of thepartner's or other database proprietor's services and is able to log animpression (e.g., an ad impression, a content impression, etc.) andprovide demographic information for that user (e.g., demographicinformation stored in the database proprietor database 142 of FIG. 1),or until all partners have been tried without a successful match. Inother examples, the partner-priority-order cookie 220 may be omitted andthe beacon instructions 214 may be configured to cause the clientcomputer 202, 203 to unconditionally send beacon requests to allavailable partners and/or other database proprietors so that all of thepartners and/or other database proprietors have an opportunity to log animpression. In yet other examples, the beacon instructions 214 may beconfigured to cause the client computer 202, 203 to receive instructionsfrom the impression monitor system 132 on an order in which to sendredirected beacon requests to one or more partners and/or other databaseproprietors.

To monitor browsing behavior and track activity of the partner cookie(s)216, the panelist computer 202 is provided with a web client meter 222.In addition, the panelist computer 202 is provided with an HTTP requestlog 224 in which the web client meter 222 may store or log HTTP requestsin association with a meter ID of the web client meter 222, user IDsoriginating from the panelist computer 202, beacon request timestamps(e.g., timestamps indicating when the panelist computer 202 sent beaconrequests such as the beacon requests 304 and 308 of FIG. 3), uniformresource locators (URLs) of websites that displayed advertisements, andad campaign IDs. In the illustrated example, the web client meter 222stores user IDs of the partner cookie(s) 216 and the panelist monitorcookie 218 in association with each logged HTTP request in the HTTPrequests log 224. In some examples, the HTTP requests log 224 canadditionally or alternatively store other types of requests such as filetransfer protocol (FTP) requests and/or any other internet protocolrequests. The web client meter 222 of the illustrated example cancommunicate such web browsing behavior or activity data in associationwith respective user IDs from the HTTP requests log 224 to the panelcollection platform 210. In some examples, the web client meter 222 mayalso be advantageously used to log impressions for untagged content oradvertisements. Unlike tagged advertisements and/or tagged content thatinclude the beacon instructions 214 causing a beacon request to be sentto the impression monitor system 132 (and/or one or more of the partners206, 208 and/or other database proprietors) identifying the exposure orimpression to the tagged content to be sent to the audience measuremententity for logging, untagged advertisements and/or advertisements do nothave such beacon instructions 214 to create an opportunity for theimpression monitor system 132 to log an impression. In such instances,HTTP requests logged by the web client meter 222 can be used to identifyany untagged content or advertisements that were rendered by the webbrowser 212 on the panelist computer 202.

In the illustrated example, the impression monitor system 132 isprovided with a user ID comparator 228, a rules/machine learning (ML)engine 230, an HTTP server 232, and a publisher/campaign/user targetdatabase 234. The user ID comparator 228 of the illustrated example isprovided to identify beacon requests from users that are panelists 114,116. In the illustrated example, the HTTP server 232 is a communicationinterface via which the impression monitor system 132 exchangesinformation (e.g., beacon requests, beacon responses, acknowledgements,failure status messages, etc.) with the client computer 202, 203. Therules/ML engine 230 and the publisher/campaign/user target database 234of the illustrated example enable the impression monitor system 132 totarget the ‘best fit’ partner (e.g., one of the partners 206 or 208) foreach impression request (or beacon request) received from the clientcomputer 202, 203. The ‘best fit’ partner is the partner most likely tohave demographic data for the user(s) of the client computer 202, 203sending the impression request. The rules/ML engine 230 is a set ofrules and machine learning classifiers generated based on evolvingempirical data stored in the publisher/campaign/user target database234. In the illustrated example, rules can be applied at the publisherlevel, publisher/campaign level, or user level. In addition, partnersmay be weighted based on their overall reach and demographic quality.

To target partners (e.g., the partners 206 and 208) at the publisherlevel of ad campaigns, the rules/ML engine 230 contains rules andclassifiers that allow the impression monitor system 132 to target the‘best fit’ partner for a particular publisher of ad campaign(s). Forexample, the impression monitoring system 132 could use an indication oftarget demographic composition(s) of publisher(s) and partner(s) (e.g.,as stored in the publisher/campaign/user target database 234) to selecta partner (e.g., one of the partners 206, 208) that is most likely tohave demographic information for a user of the client computer 202, 203requesting the impression.

To target partners (e.g., the partners 206 and 208) at the campaignlevel (e.g., a publisher has the ability to target ad campaigns based onuser demographics), the rules/ML engine 230 of the illustrated exampleare used to specify target partners at the publisher/campaign level. Forexample, if the publisher/campaign/user target database 234 storesinformation indicating that a particular ad campaign is targeted atmales aged 18 to 25, the rules/ML engine 230 uses this information toindicate a beacon request redirect to a partner most likely to have thelargest reach within this gender/age group.

To target partners (e.g., the partners 206 and 208) at the cookie level,the impression monitor system 132 updates target partner sites based onfeedback received from the partners. Such feedback could indicate userIDs that did not correspond or that did correspond to registered usersof the partner(s). In some examples, the impression monitor system 132could also update target partner sites based on user behavior. Forexample, such user behavior could be derived from analyzing cookieclickstream data corresponding to browsing activities associated withpanelist monitor cookies (e.g., the panelist monitor cookie 218). In theillustrated example, the impression monitor system 132 uses such cookieclickstream data to determine age/gender bias for particular partners bydetermining ages and genders of which the browsing behavior is moreindicative. In this manner, the impression monitor system 132 of theillustrated example can update a target or preferred partner for aparticular user or client computer 202, 203. In some examples, therules/ML engine 230 specify when to override user-level preferred targetpartners with publisher or publisher/campaign level preferred targetpartners. For example such a rule may specify an override of user-levelpreferred target partners when the user-level preferred target partnersends a number of indications that it does not have a registered usercorresponding to the client computer 202, 203 (e.g., a different user onthe client computer 202, 203 begins using a different browser having adifferent user ID in its partner cookie 216).

In the illustrated example, the impression monitor system 132 logsimpressions (e.g., ad impressions, content impressions, etc.) in animpressions per unique users table 235 based on beacon requests (e.g.,the beacon request 304 of FIG. 3) received from client computers (e.g.,the client computer 202, 203). In the illustrated example, theimpressions per unique users table 235 stores unique user IDs obtainedfrom cookies (e.g., the panelist monitor cookie 218) in association withtotal impressions per day and campaign IDs. In this manner, for eachcampaign ID, the impression monitor system 132 logs the totalimpressions per day that are attributable to a particular user or clientcomputer 202, 203.

Each of the partners 206 and 208 of the illustrated example employs anHTTP server 236 and 240 and a user ID comparator 238 and 242. In theillustrated example, the HTTP servers 236 and 240 are communicationinterfaces via which their respective partners 206 and 208 exchangeinformation (e.g., beacon requests, beacon responses, acknowledgements,failure status messages, etc.) with the client computer 202, 203. Theuser ID comparators 238 and 242 are configured to compare user cookiesreceived from a client 202, 203 against the cookie in their records toidentify the client 202, 203, if possible. In this manner, the user IDcomparators 238 and 242 can be used to determine whether users of thepanelist computer 202 have registered accounts with the partners 206 and208. If so, the partners 206 and 208 can log impressions attributed tothose users and associate those impressions with the demographics of theidentified user (e.g., demographics stored in the database proprietordatabase 142 of FIG. 1).

In the illustrated example, the panel collection platform 210 is used toidentify registered users of the partners 206, 208 that are alsopanelists 114, 116. The panel collection platform 210 can then use thisinformation to cross-reference demographic information stored by theratings entity subsystem 106 for the panelists 114, 116 with demographicinformation stored by the partners 206 and 208 for their registeredusers. The ratings entity subsystem 106 can use such cross-referencingto determine the accuracy of the demographic information collected bythe partners 206 and 208 based on the demographic information of thepanelists 114 and 116 collected by the ratings entity subsystem 106.

In some examples, the example collector 117 of the panel collectionplatform 210 collects web-browsing activity information from thepanelist computer 202. In such examples, the example collector 117requests logged data from the HTTP requests log 224 of the panelistcomputer 202 and logged data collected by other panelist computers (notshown). In addition, the collector 117 collects panelist user IDs fromthe impression monitor system 132 that the impression monitor system 132tracks as having set in panelist computers. Also, the collector 117collects partner user IDs from one or more partners (e.g., the partners206 and 208) that the partners track as having been set in panelist andnon-panelist computers. In some examples, to abide by privacy agreementsof the partners 206, 208, the collector 117 and/or the databaseproprietors 206, 208 can use a hashing technique (e.g., a double-hashingtechnique) to hash the database proprietor cookie IDs.

In some examples, the loader 118 of the panel collection platform 210analyzes and sorts the received panelist user IDs and the partner userIDs. In the illustrated example, the loader 118 analyzes received loggeddata from panelist computers (e.g., from the HTTP requests log 224 ofthe panelist computer 202) to identify panelist user IDs (e.g., thepanelist monitor cookie 218) associated with partner user IDs (e.g., thepartner cookie(s) 216). In this manner, the loader 118 can identifywhich panelists (e.g., ones of the panelists 114 and 116) are alsoregistered users of one or more of the partners 206 and 208 (e.g., thedatabase proprietor subsystem 108 of FIG. 1 having demographicinformation of registered users stored in the database proprietordatabase 142). In some examples, the panel collection platform 210operates to verify the accuracy of impressions collected by theimpression monitor system 132. In such some examples, the loader 118filters the logged HTTP beacon requests from the HTTP requests log 224that correlate with impressions of panelists logged by the impressionmonitor system 132 and identifies HTTP beacon requests logged at theHTTP requests log 224 that do not have corresponding impressions loggedby the impression monitor system 132. In this manner, the panelcollection platform 210 can provide indications of inaccurate impressionlogging by the impression monitor system 132 and/or provide impressionslogged by the web client meter 222 to fill-in impression data forpanelists 114, 116 missed by the impression monitor system 132.

In the illustrated example, the loader 118 stores overlapping users inan impressions-based panel demographics table 250. In the illustratedexample, overlapping users are users that are panelist members 114, 116and registered users of partner A 206 (noted as users P(A)) and/orregistered users of partner B 208 (noted as users P(B)). (Although onlytwo partners (A and B) are shown, this is for simplicity ofillustration, any number of partners may be represented in the table250. The impressions-based panel demographics table 250 of theillustrated example is shown storing meter IDs (e.g., of the web clientmeter 222 and web client meters of other computers), user IDs (e.g., analphanumeric identifier such as a user name, email address, etc.corresponding to the panelist monitor cookie 218 and panelist monitorcookies of other panelist computers), beacon request timestamps (e.g.,timestamps indicating when the panelist computer 202 and/or otherpanelist computers sent beacon requests such as the beacon requests 304and 308 of FIG. 3), uniform resource locators (URLs) of websites visited(e.g., websites that displayed advertisements), and ad campaign IDs. Inaddition, the loader 118 of the illustrated example stores partner userIDs that do not overlap with panelist user IDs in a partner A (P(A))cookie table 252 and a partner B (P(B)) cookie table 254.

Example processes performed by the example system 200 are describedbelow in connection with the communications flow diagram of FIG. 3 andthe flow diagrams of FIGS. 10, 11, and 12.

In the illustrated example of FIGS. 1 and 2, the ratings entitysubsystem 106 includes the impression monitor system 132, the rules/MLengine 230, the HTTP server communication interface 232, thepublisher/campaign/user target database 232, the GRP report generator130, the panel collection platform 210, the collector 117, the loader118, and the ratings entity database 120. In the illustrated example ofFIGS. 1 and 2, the impression monitor system 132, the rules/ML engine230, the HTTP server communication interface 232, thepublisher/campaign/user target database 232, the GRP report generator130, the panel collection platform 210, the collector 117, the loader118, and the ratings entity database 120 may be implemented as a singleapparatus or a two or more different apparatus. While an example mannerof implementing the impression monitor system 132, the rules/ML engine230, the HTTP server communication interface 232, thepublisher/campaign/user target database 232, the GRP report generator130, the panel collection platform 210, the collector 117, the loader118, and the ratings entity database 120 has been illustrated in FIGS. 1and 2, one or more of the impression monitor system 132, the rules/MLengine 230, the HTTP server communication interface 232, thepublisher/campaign/user target database 232, the GRP report generator130, the panel collection platform 210, the collector 117, the loader118, and the ratings entity database 120 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the impression monitor system 132, the rules/ML engine 230, theHTTP server communication interface 232, the publisher/campaign/usertarget database 232, the GRP report generator 130, the panel collectionplatform 210, the collector 117, the loader 118, and the ratings entitydatabase 120 and/or, more generally, the example apparatus of theexample ratings entity subsystem 106 may be implemented by hardware,software, firmware and/or any combination of hardware, software and/orfirmware. Thus, for example, any of the impression monitor system 132,the rules/ML engine 230, the HTTP server communication interface 232,the publisher/campaign/user target database 232, the GRP reportgenerator 130, the panel collection platform 210, the collector 117, theloader 118, and the ratings entity database 120 and/or, more generally,the example apparatus of the ratings entity subsystem 106 could beimplemented by one or more circuit(s), programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)),etc. When any of the appended apparatus or system claims are read tocover a purely software and/or firmware implementation, at least one ofthe impression monitor system 132, the rules/ML engine 230, the HTTPserver communication interface 232, the publisher/campaign/user targetdatabase 232, the GRP report generator 130, the panel collectionplatform 210, the collector 117, the loader 118, and/or the ratingsentity database 120 appearing in such claim is hereby expressly definedto include a computer readable medium such as a memory, DVD, CD, etc.storing the software and/or firmware. Further still, the exampleapparatus of the ratings entity subsystem 106 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1 and 2, and/or may include more than one of any orall of the illustrated elements, processes and devices.

Turning to FIG. 3, an example communication flow diagram shows anexample manner in which the example system 200 of FIG. 2 logsimpressions by clients (e.g., clients 202, 203). The example chain ofevents shown in FIG. 3 occurs when a client 202, 203 accesses a taggedadvertisement or tagged content. Thus, the events of FIG. 3 begin when aclient sends an HTTP request to a server for content and/or anadvertisement, which, in this example, is tagged to forward an exposurerequest to the ratings entity. In the illustrated example of FIG. 3, theweb browser of the client 202, 203 receives the requested content oradvertisement (e.g., the content or advertisement 102) from a publisher(e.g., ad publisher 302). It is to be understood that the client 202,203 often requests a webpage containing content of interest (e.g.,www.weather.com) and the requested webpage contains links to ads thatare downloaded and rendered within the webpage. The ads may come fromdifferent servers than the originally requested content. Thus, therequested content may contain instructions that cause the client 202,203 to request the ads (e.g., from the ad publisher 302) as part of theprocess of rendering the webpage originally requested by the client. Thewebpage, the ad or both may be tagged. In the illustrated example, theuniform resource locator (URL) of the ad publisher is illustrativelynamed http://my.advertiser.com.

For purposes of the following illustration, it is assumed that theadvertisement 102 is tagged with the beacon instructions 214 (FIG. 2).Initially, the beacon instructions 214 cause the web browser of theclient 202 or 203 to send a beacon request 304 to the impression monitorsystem 132 when the tagged ad is accessed. In the illustrated example,the web browser sends the beacon request 304 using an HTTP requestaddressed to the URL of the impression monitor system 132 at, forexample, a first internet domain. The beacon request 304 includes one ormore of a campaign ID, a creative type ID, and/or a placement IDassociated with the advertisement 102. In addition, the beacon request304 includes a document referrer (e.g., www.acme.com), a timestamp ofthe impression, and a publisher site ID (e.g., the URLhttp://my.advertiser.com of the ad publisher 302). In addition, if theweb browser of the client 202 or 203 contains the panelist monitorcookie 218, the beacon request 304 will include the panelist monitorcookie 218. In other example implementations, the cookie 218 may not bepassed until the client 202 or 203 receives a request sent by a serverof the impression monitor system 132 in response to, for example, theimpression monitor system 132 receiving the beacon request 304.

In response to receiving the beacon request 304, the impression monitorsystem 132 logs an impression by recording the ad identificationinformation (and any other relevant identification information)contained in the beacon request 304. In the illustrated example, theimpression monitor system 132 logs the impression regardless of whetherthe beacon request 304 indicated a user ID (e.g., based on the panelistmonitor cookie 218) that matched a user ID of a panelist member (e.g.,one of the panelists 114 and 116 of FIG. 1). However, if the user ID(e.g., the panelist monitor cookie 218) matches a user ID of a panelistmember (e.g., one of the panelists 114 and 116 of FIG. 1) set by and,thus, stored in the record of the ratings entity subsystem 106, thelogged impression will correspond to a panelist of the impressionmonitor system 132. If the user ID does not correspond to a panelist ofthe impression monitor system 132, the impression monitor system 132will still benefit from logging an impression even though it will nothave a user ID record (and, thus, corresponding demographics) for theimpression reflected in the beacon request 304.

In the illustrated example of FIG. 3, to compare or supplement panelistdemographics (e.g., for accuracy or completeness) of the impressionmonitor system 132 with demographics at partner sites and/or to enable apartner site to attempt to identify the client and/or log theimpression, the impression monitor system 132 returns a beacon responsemessage 306 (e.g., a first beacon response) to the web browser of theclient 202, 203 including an HTTP 302 redirect message and a URL of aparticipating partner at, for example, a second internet domain. In theillustrated example, the HTTP 302 redirect message instructs the webbrowser of the client 202, 203 to send a second beacon request 308 tothe particular partner (e.g., one of the partners A 206 or B 208). Inother examples, instead of using an HTTP 302 redirect message, redirectsmay instead be implemented using, for example, an iframe sourceinstructions (e.g., <iframe src=“ ”>) or any other instruction that caninstruct a web browser to send a subsequent beacon request (e.g., thesecond beacon request 308) to a partner. In the illustrated example, theimpression monitor system 132 determines the partner specified in thebeacon response 306 using its rules/ML engine 230 (FIG. 2) based on, forexample, empirical data indicative of which partner should be preferredas being most likely to have demographic data for the user ID. In otherexamples, the same partner is always identified in the first redirectmessage and that partner always redirects the client 202, 203 to thesame second partner when the first partner does not log the impression.In other words, a set hierarchy of partners is defined and followed suchthat the partners are “daisy chained” together in the same predeterminedorder rather than them trying to guess a most likely database proprietorto identify an unknown client 203.

Prior to sending the beacon response 306 to the web browser of theclient 202, 203, the impression monitor system 132 of the illustratedexample replaces a site ID (e.g., a URL) of the ad publisher 302 with amodified site ID (e.g., a substitute site ID) which is discernable onlyby the impression monitor system 132 as corresponding to the adpublisher 302. In some example implementations, the impression monitorsystem 132 may also replace the host website ID (e.g., www.acme.com)with another modified site ID (e.g., a substitute site ID) which isdiscernable only by the impression monitor system 132 as correspondingto the host website. In this way, the source(s) of the ad and/or thehost content are masked from the partners. In the illustrated example,the impression monitor system 132 maintains a publisher ID mapping table310 that maps original site IDs of ad publishers with modified (orsubstitute) site IDs created by the impression monitor system 132 toobfuscate or hide ad publisher identifiers from partner sites. In someexamples, the impression monitor system 132 also stores the host websiteID in association with a modified host website ID in a mapping table. Inaddition, the impression monitor system 132 encrypts all of theinformation received in the beacon request 304 and the modified site IDto prevent any intercepting parties from decoding the information. Theimpression monitor system 132 of the illustrated example sends theencrypted information in the beacon response 306 to the web browser 212.In the illustrated example, the impression monitor system 132 uses anencryption that can be decrypted by the selected partner site specifiedin the HTTP 302 redirect.

In some examples, the impression monitor system 132 also sends a URLscrape instruction 320 to the client computer 202, 302. In suchexamples, the URL scrape instruction 320 causes the client computer 202,203 to “scrape” the URL of the webpage or website associated with thetagged advertisement 102. For example, the client computer 202, 203 mayperform scraping of web page URLs by reading text rendered or displayedat a URL address bar of the web browser 212. The client computer 202,203 then sends a scraped URL 322 to the impression monitor system 322.In the illustrated example, the scraped URL 322 indicates the hostwebsite (e.g., http://www.acme.com) that was visited by a user of theclient computer 202, 203 and in which the tagged advertisement 102 wasdisplayed. In the illustrated example, the tagged advertisement 102 isdisplayed via an ad iFrame having a URL ‘my.advertiser.com,’ whichcorresponds to an ad network (e.g., the publisher 302) that serves thetagged advertisement 102 on one or more host websites. However, in theillustrated example, the host website indicated in the scraped URL 322is ‘www.acme.com,’ which corresponds to a website visited by a user ofthe client computer 202, 203.

URL scraping is particularly useful under circumstances in which thepublisher is an ad network from which an advertiser bought advertisementspace/time. In such instances, the ad network dynamically selects fromsubsets of host websites (e.g., www.caranddriver.com, www.espn.com,www.allrecipes.com, etc.) visited by users on which to display ads viaad iFrames. However, the ad network cannot foretell definitively thehost websites on which the ad will be displayed at any particular time.In addition, the URL of an ad iFrame in which the tagged advertisement102 is being rendered may not be useful to identify the topic of a hostwebsite (e.g., www.acme.com in the example of FIG. 3) rendered by theweb browser 212. As such, the impression monitor system 132 may not knowthe host website in which the ad iFrame is displaying the taggedadvertisement 102.

The URLs of host websites (e.g., www.caranddriver.com, www.espn.com,www.allrecipes.com, etc.) can be useful to determine topical interests(e.g., automobiles, sports, cooking, etc.) of user(s) of the clientcomputer 202, 203. In some examples, audience measurement entities canuse host website URLs to correlate with user/panelist demographics andinterpolate logged impressions to larger populations based ondemographics and topical interests of the larger populations and basedon the demographics and topical interests of users/panelists for whichimpressions were logged. Thus, in the illustrated example, when theimpression monitor system 132 does not receive a host website URL orcannot otherwise identify a host website URL based on the beacon request304, the impression monitor system 132 sends the URL scrape instruction320 to the client computer 202, 203 to receive the scraped URL 322. Inthe illustrated example, if the impression monitor system 132 canidentify a host website URL based on the beacon request 304, theimpression monitor system 132 does not send the URL scrape instruction320 to the client computer 202, 203, thereby, conserving network andcomputer bandwidth and resources.

In response to receiving the beacon response 306, the web browser of theclient 202, 203 sends the beacon request 308 to the specified partnersite, which is the partner A 206 (e.g., a second internet domain) in theillustrated example. The beacon request 308 includes the encryptedparameters from the beacon response 306. The partner A 206 (e.g.,Facebook) decrypts the encrypted parameters and determines whether theclient matches a registered user of services offered by the partner A206. This determination involves requesting the client 202, 203 to passany cookie (e.g., one of the partner cookies 216 of FIG. 2) it storesthat had been set by partner A 206 and attempting to match the receivedcookie against the cookies stored in the records of partner A 206. If amatch is found, partner A 206 has positively identified a client 202,203. Accordingly, the partner A 206 site logs an impression inassociation with the demographics information of the identified client.This log (which includes the undetectable source identifier) issubsequently provided to the ratings entity for processing into GRPs asdiscussed below. In the event partner A 206 is unable to identify theclient 202, 203 in its records (e.g., no matching cookie), the partner A206 does not log an impression.

In some example implementations, if the user ID does not match aregistered user of the partner A 206, the partner A 206 may return abeacon response 312 (e.g., a second beacon response) including a failureor non-match status or may not respond at all, thereby terminating theprocess of FIG. 3. However, in the illustrated example, if partner A 206cannot identify the client 202, 203, partner A 206 returns a second HTTP302 redirect message in the beacon response 312 (e.g., the second beaconresponse) to the client 202, 203. For example, if the partner A site 206has logic (e.g., similar to the rules/ml engine 230 of FIG. 2) tospecify another partner (e.g., partner B 208 or any other partner) whichmay likely have demographics for the user ID, then the beacon response312 may include an HTTP 302 redirect (or any other suitable instructionto cause a redirected communication) along with the URL of the otherpartner (e.g., at a third internet domain). Alternatively, in the daisychain approach discussed above, the partner A site 206 may alwaysredirect to the same next partner or database proprietor (e.g., partnerB 208 at, for example, a third internet domain or a non-partnereddatabase proprietor subsystem 110 of FIG. 1 at a third internet domain)whenever it cannot identify the client 202, 203. When redirecting, thepartner A site 206 of the illustrated example encrypts the ID,timestamp, referrer, etc. parameters using an encryption that can bedecoded by the next specified partner.

As a further alternative, if the partner A site 206 does not have logicto select a next best suited partner likely to have demographics for theuser ID and is not effectively daisy chained to a next partner bystoring instructions that redirect to a partner entity, the beaconresponse 312 can redirect the client 202, 203 to the impression monitorsystem 132 with a failure or non-match status. In this manner, theimpression monitor system 132 can use its rules/ML engine 230 to selecta next-best suited partner to which the web browser of the client 202,203 should send a beacon request (or, if no such logic is provided,simply select the next partner in a hierarchical (e.g., fixed) list). Inthe illustrated example, the impression monitor system 132 selects thepartner B site 208, and the web browser of the client 202, 203 sends abeacon request to the partner B site 208 with parameters encrypted in amanner that can be decrypted by the partner B site 208. The partner Bsite 208 then attempts to identify the client 202, 203 based on its owninternal database. If a cookie obtained from the client 202, 203 matchesa cookie in the records of partner B 208, partner B 208 has positivelyidentified the client 202, 203 and logs the impression in associationwith the demographics of the client 202, 203 for later provision to theimpression monitor system 132. In the event that partner B 208 cannotidentify the client 202, 203, the same process of failure notificationor further HTTP 302 redirects may be used by the partner B 208 toprovide a next other partner site an opportunity to identify the clientand so on in a similar manner until a partner site identifies the client202, 203 and logs the impression, until all partner sites have beenexhausted without the client being identified, or until a predeterminednumber of partner sites failed to identify the client 202, 203.

Using the process illustrated in FIG. 3, impressions (e.g., adimpressions, content impressions, etc.) can be mapped to correspondingdemographics even when the impressions are not triggered by panelmembers associated with the audience measurement entity (e.g., ratingsentity subsystem 106 of FIG. 1). That is, during an impressioncollection or merging process, the panel collection platform 210 of theratings entity can collect distributed impressions logged by (1) theimpression monitor system 132 and (2) any participating partners (e.g.,partners 206, 208). As a result, the collected data covers a largerpopulation with richer demographics information than has heretofore beenpossible. Consequently, generating accurate, consistent, and meaningfulonline GRPs is possible by pooling the resources of the distributeddatabases as described above. The example structures of FIGS. 2 and 3generate online GRPs based on a large number of combined demographicdatabases distributed among unrelated parties (e.g., Nielsen andFacebook). The end result appears as if users attributable to the loggedimpressions were part of a large virtual panel formed of registeredusers of the audience measurement entity because the selection of theparticipating partner sites can be tracked as if they were members ofthe audience measurement entities panels 114, 116. This is accomplishedwithout violating the cookie privacy protocols of the Internet.

Periodically or aperiodically, the impression data collected by thepartners (e.g., partners 206, 208) is provided to the ratings entity viaa panel collection platform 210. As discussed above, some user IDs maynot match panel members of the impression monitor system 132, but maymatch registered users of one or more partner sites. During a datacollecting and merging process to combine demographic and impressiondata from the ratings entity subsystem 106 and the partner subsystem(s)108 and 110 of FIG. 1, user IDs of some impressions logged by one ormore partners may match user IDs of impressions logged by the impressionmonitor system 132, while others (most likely many others) will notmatch. In some example implementations, the ratings entity subsystem 106may use the demographics-based impressions from matching user ID logsprovided by partner sites to assess and/or improve the accuracy of itsown demographic data, if necessary. For the demographics-basedimpressions associated with non-matching user ID logs, the ratingsentity subsystem 106 may use the impressions (e.g., advertisementimpressions, content impressions, etc.) to derive demographics-basedonline GRPs even though such impressions are not associated withpanelists of the ratings entity subsystem 106.

As briefly mentioned above, example methods, apparatus, and/or articlesof manufacture disclosed herein may be configured to preserve userprivacy when sharing demographic information (e.g., account records orregistration information) between different entities (e.g., between theratings entity subsystem 106 and the database proprietor subsystem 108).In some example implementations, a double encryption technique may beused based on respective secret keys for each participating partner orentity (e.g., the subsystems 106, 108, 110). For example, the ratingsentity subsystem 106 can encrypt its user IDs (e.g., email addresses)using its secret key and the database proprietor subsystem 108 canencrypt its user IDs using its secret key. For each user ID, therespective demographics information is then associated with theencrypted version of the user ID. Each entity then exchanges theirdemographics lists with encrypted user IDs. Because neither entity knowsthe other's secret key, they cannot decode the user IDs, and thus, theuser IDs remain private. Each entity then proceeds to perform a secondencryption of each encrypted user ID using their respective keys. Eachtwice-encrypted (or double encrypted) user ID (UID) will be in the formof E1(E2(UID)) and E2(E1(UID)), where E1 represents the encryption usingthe secret key of the ratings entity subsystem 106 and E2 represents theencryption using the secret key of the database proprietor subsystem108. Under the rule of commutative encryption, the encrypted user IDscan be compared on the basis that E1(E2(UID))=E2(E1(UID)). Thus, theencryption of user IDs present in both databases will match after thedouble encryption is completed. In this manner, matches between userrecords of the panelists and user records of the database proprietor(e.g., identifiers of registered social network users) can be comparedwithout the partner entities needing to reveal user IDs to one another.

The ratings entity subsystem 106 performs a daily impressions and UUID(cookies) totalization based on impressions and cookie data collected bythe impression monitor system 132 of FIG. 1 and the impressions loggedby the partner sites. In the illustrated example, the ratings entitysubsystem 106 may perform the daily impressions and UUID (cookies)totalization based on cookie information collected by the ratings entitycookie collector 134 of FIG. 1 and the logs provided to the panelcollection platform 210 by the partner sites. FIG. 4 depicts an exampleratings entity impressions table 400 showing quantities of impressionsto monitored users. Similar tables could be compiled for one or more ofadvertisement impressions, content impressions, or other impressions. Inthe illustrated example, the ratings entity impressions table 400 isgenerated by the ratings entity subsystem 106 for an advertisementcampaign (e.g., one or more of the advertisements 102 of FIG. 1) todetermine frequencies of impressions per day for each user.

To track frequencies of impressions per unique user per day, the ratingsentity impressions table 400 is provided with a frequency column 402. Afrequency of 1 indicates one exposure per day of an ad in an ad campaignto a unique user, while a frequency of 4 indicates four exposures perday of one or more ads in the same ad campaign to a unique user. Totrack the quantity of unique users to which impressions areattributable, the ratings impressions table 400 is provided with a UUIDscolumn 404. A value of 100,000 in the UUIDs column 404 is indicative of100,000 unique users. Thus, the first entry of the ratings entityimpressions table 400 indicates that 100,000 unique users (i.e.,UUIDs=100,000) were exposed once (i.e., frequency=1) in a single day toa particular one of the advertisements 102.

To track impressions based on exposure frequency and UUIDs, the ratingsentity impressions table 400 is provided with an impressions column 406.Each impression count stored in the impressions column 406 is determinedby multiplying a corresponding frequency value stored in the frequencycolumn 402 with a corresponding UUID value stored in the UUID column404. For example, in the second entry of the ratings entity impressionstable 400, the frequency value of two is multiplied by 200,000 uniqueusers to determine that 400,000 impressions are attributable to aparticular one of the advertisements 102.

Turning to FIG. 5, in the illustrated example, each of the partnereddatabase proprietor subsystems 108, 110 of the partners 206, 208generates and reports a database proprietor ad campaign-level age/genderand impression composition table 500 to the GRP report generator 130 ofthe ratings entity subsystem 106 on a daily basis. Similar tables can begenerated for content and/or other media. Additionally or alternatively,media in addition to advertisements may be added to the table 500. Inthe illustrated example, the partners 206, 208 tabulate the impressiondistribution by age and gender composition as shown in FIG. 5. Forexample, referring to FIG. 1, the database proprietor database 142 ofthe partnered database proprietor subsystem 108 stores loggedimpressions and corresponding demographic information of registeredusers of the partner A 206, and the database proprietor subsystem 108 ofthe illustrated example processes the impressions and correspondingdemographic information using the rules 144 to generate the DP summarytables 146 including the database proprietor ad campaign-levelage/gender and impression composition table 500.

The age/gender and impression composition table 500 is provided with anage/gender column 502, an impressions column 504, a frequency column506, and an impression composition column 508. The age/gender column 502of the illustrated example indicates the different age/genderdemographic groups. The impressions column 504 of the illustratedexample stores values indicative of the total impressions for aparticular one of the advertisements 102 (FIG. 1) for correspondingage/gender demographic groups. The frequency column 506 of theillustrated example stores values indicative of the frequency ofexposure per user for the one of the advertisements 102 that contributedto the impressions in the impressions column 504. The impressionscomposition column 508 of the illustrated example stores the percentageof impressions for each of the age/gender demographic groups.

In some examples, the database proprietor subsystems 108, 110 mayperform demographic accuracy analyses and adjustment processes on itsdemographic information before tabulating final results ofimpression-based demographic information in the database proprietorcampaign-level age/gender and impression composition table. This can bedone to address a problem facing online audience measurement processesin that the manner in which registered users represent themselves toonline data proprietors (e.g., the partners 206 and 208) is notnecessarily veridical (e.g., truthful and/or accurate). In someinstances, example approaches to online measurement that leverageaccount registrations at such online database proprietors to determinedemographic attributes of an audience may lead to inaccuratedemographic-exposure results if they rely on self-reporting ofpersonal/demographic information by the registered users during accountregistration at the database proprietor site. There may be numerousreasons for why users report erroneous or inaccurate demographicinformation when registering for database proprietor services. Theself-reporting registration processes used to collect the demographicinformation at the database proprietor sites (e.g., social media sites)does not facilitate determining the veracity of the self-reporteddemographic information. To analyze and adjust inaccurate demographicinformation, the ratings entity subsystem 106 and the databaseproprietor subsystems 108, 110 may use example methods, systems,apparatus, and/or articles of manufacture disclosed in U.S. patentapplication Ser. No. 13/209,292, filed on Aug. 12, 2011, and titled“Methods and Apparatus to Analyze and Adjust Demographic Information,”which is hereby incorporated herein by reference in its entirety.

Turning to FIG. 6, in the illustrated example, the ratings entitysubsystem 106 generates a panelist ad campaign-level age/gender andimpression composition table 600 on a daily basis. Similar tables can begenerated for content and/or other media. Additionally or alternatively,media in addition to advertisements may be added to the table 600. Theexample ratings entity subsystem 106 tabulates the impressiondistribution by age and gender composition as shown in FIG. 6 in thesame manner as described above in connection with FIG. 5. As shown inFIG. 6, the panelist ad campaign-level age/gender and impressioncomposition table 600 also includes an age/gender column 602, animpressions column 604, a frequency column 606, and an impressioncomposition column 608. In the illustrated example of FIG. 6, theimpressions are calculated based on the PC and TV panelists 114 andonline panelists 116.

After creating the campaign-level age/gender and impression compositiontables 500 and 600 of FIGS. 5 and 6, the ratings entity subsystem 106creates a combined campaign-level age/gender and impression compositiontable 700 shown in FIG. 7. In particular, the ratings entity subsystem106 combines the impression composition percentages from the impressioncomposition columns 508 and 608 of FIGS. 5 and 6 to compare theage/gender impression distribution differences between the ratingsentity panelists and the social network users.

As shown in FIG. 7, the combined campaign-level age/gender andimpression composition table 700 includes an error weighted column 702,which stores mean squared errors (MSEs) indicative of differencesbetween the impression compositions of the ratings entity panelists andthe users of the database proprietor (e.g., social network users).Weighted MSEs can be determined using Equation 4 below.

Weighted MSE=(α*IC_((RE))+(1−α)IC_((DP)))  Equation 4

In Equation 4 above, a weighting variable (α) represents the ratio ofMSE(SN)/MSE(RE) or some other function that weights the compositionsinversely proportional to their MSE. As shown in Equation 4, theweighting variable (α) is multiplied by the impression composition ofthe ratings entity (IC_((RE))) to generate a ratings entity weightedimpression composition (α*IC_((RE))). The impression composition of thedatabase proprietor (e.g., a social network) (IC_((DP))) is thenmultiplied by a difference between one and the weighting variable (α) todetermine a database proprietor weighted impression composition ((1−α)IC_((DP))).

In the illustrated example, the ratings entity subsystem 106 can smoothor correct the differences between the impression compositions byweighting the distribution of MSE. The MSE values account for samplesize variations or bounces in data caused by small sample sizes.

Turning to FIG. 8, the ratings entity subsystem 106 determines reach anderror-corrected impression compositions in an age/gender impressionsdistribution table 800. The age/gender impressions distribution table800 includes an age/gender column 802, an impressions column 804, afrequency column 806, a reach column 808, and an impressions compositioncolumn 810. The impressions column 804 stores error-weighted impressionsvalues corresponding to impressions tracked by the ratings entitysubsystem 106 (e.g., the impression monitor system 132 and/or the panelcollection platform 210 based on impressions logged by the web clientmeter 222). In particular, the values in the impressions column 804 arederived by multiplying weighted MSE values from the error weightedcolumn 702 of FIG. 7 with corresponding impressions values from theimpressions column 604 of FIG. 6.

The frequency column 806 stores frequencies of impressions as tracked bythe database proprietor subsystem 108. The frequencies of impressionsare imported into the frequency column 806 from the frequency column 506of the database proprietor campaign-level age/gender and impressioncomposition table 500 of FIG. 5. For age/gender groups missing from thetable 500, frequency values are taken from the ratings entitycampaign-level age/gender and impression composition table 600 of FIG.6. For example, the database proprietor campaign-level age/gender andimpression composition table 500 does not have a less than 12 (<12)age/gender group. Thus, a frequency value of 3 is taken from the ratingsentity campaign-level age/gender and impression composition table 600.

The reach column 808 stores reach values representing reach of one ormore of the content and/or advertisements 102 (FIG. 1) for eachage/gender group. The reach values are determined by dividing respectiveimpressions values from the impressions column 804 by correspondingfrequency values from the frequency column 806. The impressionscomposition column 810 stores values indicative of the percentage ofimpressions per age/gender group. In the illustrated example, the finaltotal frequency in the frequency column 806 is equal to the totalimpressions divided by the total reach.

FIGS. 9, 10, 11, 12, and 14 are flow diagrams representative of machinereadable instructions that can be executed to implement the methods andapparatus described herein. The example processes of FIGS. 9, 10, 11,12, and 14 may be implemented using machine readable instructions that,when executed, cause a device (e.g., a programmable controller,processor, other programmable machine, integrated circuit, or logiccircuit) to perform the operations shown in FIGS. 9, 10, 11, 12, and 14.For instance, the example processes of FIGS. 9, 10, 11, 12, and 14 maybe performed using a processor, a controller, and/or any other suitableprocessing device. For example, the example process of FIGS. 9, 10, 11,12, and 14 may be implemented using coded instructions stored on atangible machine readable medium such as a flash memory, a read-onlymemory (ROM), and/or a random-access memory (RAM).

As used herein, the term tangible computer readable medium is expresslydefined to include any type of computer readable storage and to excludepropagating signals. Additionally or alternatively, the exampleprocesses of FIGS. 9, 10, 11, 12, and 14 may be implemented using codedinstructions (e.g., computer readable instructions) stored on anon-transitory computer readable medium such as a flash memory, aread-only memory (ROM), a random-access memory (RAM), a cache, or anyother storage media in which information is stored for any duration(e.g., for extended time periods, permanently, brief instances, fortemporarily buffering, and/or for caching of the information). As usedherein, the term non-transitory computer readable medium is expresslydefined to include any type of computer readable medium and to excludepropagating signals.

Alternatively, the example processes of FIGS. 9, 10, 11, 12, and 14 maybe implemented using any combination(s) of application specificintegrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)),field programmable logic device(s) (FPLD(s)), discrete logic, hardware,firmware, etc. Also, the example processes of FIGS. 9, 10, 11, 12, and14 may be implemented as any combination(s) of any of the foregoingtechniques, for example, any combination of firmware, software, discretelogic and/or hardware.

Although the example processes of FIGS. 9, 10, 11, 12, and 14 aredescribed with reference to the flow diagrams of FIGS. 9, 10, 11, 12,and 14, other methods of implementing the processes of FIGS. 9, 10, 11,12, and 14 may be employed. For example, the order of execution of theblocks may be changed, and/or some of the blocks described may bechanged, eliminated, sub-divided, or combined. Additionally, one or bothof the example processes of FIGS. 9, 10, 11, 12, and 14 may be performedsequentially and/or in parallel by, for example, separate processingthreads, processors, devices, discrete logic, circuits, etc.

Turning in detail to FIG. 9, the ratings entity subsystem 106 of FIG. 1may perform the depicted process to collect demographics and impressiondata from partners and to assess the accuracy and/or adjust its owndemographics data of its panelists 114, 116. The example process of FIG.9 collects demographics and impression data for registered users of oneor more partners (e.g., the partners 206 and 208 of FIGS. 2 and 3) thatoverlap with panelist members (e.g., the panelists 114 and 116 ofFIG. 1) of the ratings entity subsystem 106 as well as demographics andimpression data from partner sites that correspond to users that are notregistered panel members of the ratings entity subsystem 106. Thecollected data is combined with other data collected at the ratingsentity to determine online GRPs. The example process of FIG. 9 isdescribed in connection with the example system 100 of FIG. 1 and theexample system 200 of FIG. 2.

Initially, the GRP report generator 130 (FIG. 1) receives impressionsper unique users 235 (FIG. 2) from the impression monitor system 132(block 902). The GRP report generator 130 receives impressions-basedaggregate demographics (e.g., the partner campaign-level age/gender andimpression composition table 500 of FIG. 5) from one or more partner(s)(block 904). In the illustrated example, user IDs of registered users ofthe partners 206, 208 are not received by the GRP report generator 130.Instead, the partners 206, 208 remove user IDs and aggregateimpressions-based demographics in the partner campaign-level age/genderand impression composition table 500 at demographic bucket levels (e.g.,males aged 13-18, females aged 13-18, etc.). However, for instances inwhich the partners 206, 208 also send user IDs to the GRP reportgenerator 130, such user IDs are exchanged in an encrypted format basedon, for example, the double encryption technique described above.

For examples in which the impression monitor system 132 modifies siteIDs and sends the modified site IDs in the beacon response 306, thepartner(s) log impressions based on those modified site IDs. In suchexamples, the impressions collected from the partner(s) at block 904 areimpressions logged by the partner(s) against the modified site IDs. Whenthe ratings entity subsystem 106 receives the impressions with modifiedsite IDs, GRP report generator 130 identifies site IDs for theimpressions received from the partner(s) (block 906). For example, theGRP report generator 130 uses the site ID map 310 (FIG. 3) generated bythe impression monitoring system 310 during the beacon receive andresponse process (e.g., discussed above in connection with FIG. 3) toidentify the actual site IDs corresponding to the modified site IDs inthe impressions received from the partner(s).

The GRP report generator 130 receives per-panelist impressions-baseddemographics (e.g., the impressions-based panel demographics table 250of FIG. 2) from the panel collection platform 210 (block 908). In theillustrated example, per-panelist impressions-based demographics areimpressions logged in association with respective user IDs of panelist114, 116 (FIG. 1) as shown in the impressions-based panel demographicstable 250 of FIG. 2.

The GRP report generator 130 removes duplicate impressions between theper-panelist impressions-based panel demographics 250 received at block908 from the panel collection platform 210 and the impressions perunique users 235 received at block 902 from the impression monitorsystem 132 (block 910). In this manner, duplicate impressions logged byboth the impression monitor system 132 and the web client meter 222(FIG. 2) will not skew GRPs generated by the GRP generator 130. Inaddition, by using the per-panelist impressions-based panel demographics250 from the panel collection platform 210 and the impressions perunique users 235 from the impression monitor system 132, the GRPgenerator 130 has the benefit of impressions from redundant systems(e.g., the impression monitor system 132 and the web client meter 222).In this manner, if one of the systems (e.g., one of the impressionmonitor system 132 or the web client meter 222) misses one or moreimpressions, the record(s) of such impression(s) can be obtained fromthe logged impressions of the other system (e.g., the other one of theimpression monitor system 132 or the web client meter 222).

The GRP report generator 130 generates an aggregate of theimpressions-based panel demographics 250 (block 912). For example, theGRP report generator 130 aggregates the impressions-based paneldemographics 250 into demographic bucket levels (e.g., males aged 13-18,females aged 13-18, etc.) to generate the panelist ad campaign-levelage/gender and impression composition table 600 of FIG. 6.

In some examples, the GRP report generator 130 does not use theper-panelist impressions-based panel demographics from the panelcollection platform 210. In such instances, the ratings entity subsystem106 does not rely on web client meters such as the web client meter 222of FIG. 2 to determine GRP using the example process of FIG. 9. Insteadin such instances, the GRP report generator 130 determines impressionsof panelists based on the impressions per unique users 235 received atblock 902 from the impression monitor system 132 and uses the results toaggregate the impressions-based panel demographics at block 912. Forexample, as discussed above in connection with FIG. 2, the impressionsper unique users table 235 stores panelist user IDs in association withtotal impressions and campaign IDs. As such, the GRP report generator130 may determine impressions of panelists based on the impressions perunique users 235 without using the impression-based panel demographics250 collected by the web client meter 222.

The GRP report generator 130 combines the impressions-based aggregatedemographic data from the partner(s) 206, 208 (received at block 904)and the panelists 114, 116 (generated at block 912) its demographic datawith received demographic data (block 914). For example, the GRP reportgenerator 130 of the illustrated example combines the impressions-basedaggregate demographic data to form the combined campaign-levelage/gender and impression composition table 700 of FIG. 7.

The GRP report generator 130 determines distributions for theimpressions-based demographics of block 914 (block 916). In theillustrated example, the GRP report generator 130 stores thedistributions of the impressions-based demographics in the age/genderimpressions distribution table 800 of FIG. 8. In addition, the GRPreport generator 130 generates online GRPs based on theimpressions-based demographics (block 918). In the illustrated example,the GRP report generator 130 uses the GRPs to create one or more of theGRP report(s) 131. In some examples, the ratings entity subsystem 106sells or otherwise provides the GRP report(s) 131 to advertisers,publishers, content providers, manufacturers, and/or any other entityinterested in such market research. The example process of FIG. 9 thenends.

Turning now to FIG. 10, the depicted example flow diagram may beperformed by a client computer 202, 203 (FIGS. 2 and 3) to route beaconrequests (e.g., the beacon requests 304, 308 of FIG. 3) to web serviceproviders to log demographics-based impressions. Initially, the clientcomputer 202, 203 receives tagged content and/or a tagged advertisement102 (block 1002) and sends the beacon request 304 to the impressionmonitor system 132 (block 1004) to give the impression monitor system132 (e.g., at a first internet domain) an opportunity to log animpression for the client computer 202, 203. The client computer 202,203 begins a timer (block 1006) based on a time for which to wait for aresponse from the impression monitor system 132.

If a timeout has not expired (block 1008), the client computer 202, 203determines whether it has received a redirection message (block 1010)from the impression monitor system 132 (e.g., via the beacon response306 of FIG. 3). If the client computer 202, 203 has not received aredirection message (block 1010), control returns to block 1008. Controlremains at blocks 1008 and 1010 until either (1) a timeout has expired,in which case control advances to block 1016 or (2) the client computer202, 203 receives a redirection message.

If the client computer 202, 203 receives a redirection message at block1010, the client computer 202, 203 sends the beacon request 308 to apartner specified in the redirection message (block 1012) to give thepartner an opportunity to log an impression for the client computer 202,203. During a first instance of block 1012 for a particular taggedadvertisement (e.g., the tagged advertisement 102), the partner (or insome examples, non-partnered database proprietor 110) specified in theredirection message corresponds to a second internet domain. Duringsubsequent instances of block 1012 for the same tagged advertisement, asbeacon requests are redirected to other partner or non-partnereddatabase proprietors, such other partner or non-partnered databaseproprietors correspond to third, fourth, fifth, etc. internet domains.In some examples, the redirection message(s) may specify anintermediary(ies) (e.g., an intermediary(ies) server(s) or sub-domainserver(s)) associated with a partner(s) and/or the client computer 202,203 sends the beacon request 308 to the intermediary(ies) based on theredirection message(s) as described below in conjunction with FIG. 13.

The client computer 202, 203 determines whether to attempt to sendanother beacon request to another partner (block 1014). For example, theclient computer 202, 203 may be configured to send a certain number ofbeacon requests in parallel (e.g., to send beacon requests to two ormore partners at roughly the same time rather than sending one beaconrequest to a first partner at a second internet domain, waiting for areply, then sending another beacon request to a second partner at athird internet domain, waiting for a reply, etc.) and/or to wait for aredirection message back from a current partner to which the clientcomputer 202, 203 sent the beacon request at block 1012. If the clientcomputer 202, 203 determines that it should attempt to send anotherbeacon request to another partner (block 1014), control returns to block1006.

If the client computer 202, 203 determines that it should not attempt tosend another beacon request to another partner (block 1014) or after thetimeout expires (block 1008), the client computer 202, 203 determineswhether it has received the URL scrape instruction 320 (FIG. 3) (block1016). If the client computer 202, 203 did not receive the URL scrapeinstruction 320 (block 1016), control advances to block 1022. Otherwise,the client computer 202, 203 scrapes the URL of the host websiterendered by the web browser 212 (block 1018) in which the tagged contentand/or advertisement 102 is displayed or which spawned the taggedcontent and/or advertisement 102 (e.g., in a pop-up window). The clientcomputer 202, 203 sends the scraped URL 322 to the impression monitorsystem 132 (block 1020). Control then advances to block 1022, at whichthe client computer 202, 203 determines whether to end the exampleprocess of FIG. 10. For example, if the client computer 202, 203 is shutdown or placed in a standby mode or if its web browser 212 (FIGS. 2 and3) is shut down, the client computer 202, 203 ends the example processof FIG. 10. If the example process is not to be ended, control returnsto block 1002 to receive another content and/or tagged ad. Otherwise,the example process of FIG. 10 ends.

In some examples, real-time redirection messages from the impressionmonitor system 132 may be omitted from the example process of FIG. 10,in which cases the impression monitor system 132 does not send redirectinstructions to the client computer 202, 203. Instead, the clientcomputer 202, 203 refers to its partner-priority-order cookie 220 todetermine partners (e.g., the partners 206 and 208) to which it shouldsend redirects and the ordering of such redirects. In some examples, theclient computer 202, 203 sends redirects substantially simultaneously toall partners listed in the partner-priority-order cookie 220 (e.g., inseriatim, but in rapid succession, without waiting for replies). In suchsome examples, block 1010 is omitted and at block 1012, the clientcomputer 202, 203 sends a next partner redirect based on thepartner-priority-order cookie 220. In some such examples, blocks 1006and 1008 may also be omitted, or blocks 1006 and 1008 may be kept toprovide time for the impression monitor system 132 to provide the URLscrape instruction 320 at block 1016.

Turning to FIG. 11, the example flow diagram may be performed by theimpression monitor system 132 (FIGS. 2 and 3) to log impressions and/orredirect beacon requests to web service providers (e.g., databaseproprietors) to log impressions. Initially, the impression monitorsystem 132 waits until it has received a beacon request (e.g., thebeacon request 304 of FIG. 3) (block 1102). The impression monitorsystem 132 of the illustrated example receives beacon requests via theHTTP server 232 of FIG. 2. When the impression monitor system 132receives a beacon request (block 1102), it determines whether a cookie(e.g., the panelist monitor cookie 218 of FIG. 2) was received from theclient computer 202, 203 (block 1104). For example, if a panelistmonitor cookie 218 was previously set in the client computer 202, 203,the beacon request sent by the client computer 202, 203 to the panelistmonitoring system will include the cookie.

If the impression monitor system 132 determines at block 1104 that itdid not receive the cookie in the beacon request (e.g., the cookie wasnot previously set in the client computer 202, 203, the impressionmonitor system 132 sets a cookie (e.g., the panelist monitor cookie 218)in the client computer 202, 203 (block 1106). For example, theimpression monitor system 132 may use the HTTP server 232 to send back aresponse to the client computer 202, 203 to ‘set’ a new cookie (e.g.,the panelist monitor cookie 218).

After setting the cookie (block 1106) or if the impression monitorsystem 132 did receive the cookie in the beacon request (block 1104),the impression monitor system 132 logs an impression (block 1108). Theimpression monitor system 132 of the illustrated example logs animpression in the impressions per unique users table 235 of FIG. 2. Asdiscussed above, the impression monitor system 132 logs the impressionregardless of whether the beacon request corresponds to a user ID thatmatches a user ID of a panelist member (e.g., one of the panelists 114and 116 of FIG. 1). However, if the user ID comparator 228 (FIG. 2)determines that the user ID (e.g., the panelist monitor cookie 218)matches a user ID of a panelist member (e.g., one of the panelists 114and 116 of FIG. 1) set by and, thus, stored in the record of the ratingsentity subsystem 106, the logged impression will correspond to apanelist of the impression monitor system 132. For such examples inwhich the user ID matches a user ID of a panelist, the impressionmonitor system 132 of the illustrated example logs a panelist identifierwith the impression in the impressions per unique users table 235 andsubsequently an audience measurement entity associates the knowndemographics of the corresponding panelist (e.g., a corresponding one ofthe panelists 114, 116) with the logged impression based on the panelistidentifier. Such associations between panelist demographics (e.g., theage/gender column 602 of FIG. 6) and logged impression data are shown inthe panelist ad campaign-level age/gender and impression compositiontable 600 of FIG. 6. If the user ID comparator 228 (FIG. 2) determinesthat the user ID does not correspond to a panelist 114, 116, theimpression monitor system 132 will still benefit from logging animpression (e.g., an ad impression or content impression) even though itwill not have a user ID record (and, thus, corresponding demographics)for the impression reflected in the beacon request 304.

The impression monitor system 132 selects a next partner (block 1110).For example, the impression monitor system 132 may use the rules/MLengine 230 (FIG. 2) to select one of the partners 206 or 208 of FIGS. 2and 3 at random or based on an ordered listing or ranking of thepartners 206 and 208 for an initial redirect in accordance with therules/ML engine 230 (FIG. 2) and to select the other one of the partners206 or 208 for a subsequent redirect during a subsequent execution ofblock 1110.

The impression monitor system 132 sends a beacon response (e.g., thebeacon response 306) to the client computer 202, 203 including an HTTP302 redirect (or any other suitable instruction to cause a redirectedcommunication) to forward a beacon request (e.g., the beacon request 308of FIG. 3) to a next partner (e.g., the partner A 206 of FIG. 2) (block1112) and starts a timer (block 1114). The impression monitor system 132of the illustrated example sends the beacon response 306 using the HTTPserver 232. In the illustrated example, the impression monitor system132 sends an HTTP 302 redirect (or any other suitable instruction tocause a redirected communication) at least once to allow at least apartner site (e.g., one of the partners 206 or 208 of FIGS. 2 and 3) toalso log an impression for the same advertisement (or content). However,in other example implementations, the impression monitor system 132 mayinclude rules (e.g., as part of the rules/ML engine 230 of FIG. 2) toexclude some beacon requests from being redirected. The timer set atblock 1114 is used to wait for real-time feedback from the next partnerin the form of a fail status message indicating that the next partnerdid not find a match for the client computer 202, 203 in its records.

If the timeout has not expired (block 1116), the impression monitorsystem 132 determines whether it has received a fail status message(block 1118). Control remains at blocks 1116 and 1118 until either (1) atimeout has expired, in which case control returns to block 1102 toreceive another beacon request or (2) the impression monitor system 132receives a fail status message.

If the impression monitor system 132 receives a fail status message(block 1118), the impression monitor system 132 determines whether thereis another partner to which a beacon request should be sent (block 1120)to provide another opportunity to log an impression. The impressionmonitor system 132 may select a next partner based on a smart selectionprocess using the rules/ML engine 230 of FIG. 2 or based on a fixedhierarchy of partners. If the impression monitor system 132 determinesthat there is another partner to which a beacon request should be sent,control returns to block 1110. Otherwise, the example process of FIG. 11ends.

In some examples, real-time feedback from partners may be omitted fromthe example process of FIG. 11 and the impression monitor system 132does not send redirect instructions to the client computer 202, 203.Instead, the client computer 202, 203 refers to itspartner-priority-order cookie 220 to determine partners (e.g., thepartners 206 and 208) to which it should send redirects and the orderingof such redirects. In some examples, the client computer 202, 203 sendsredirects simultaneously to all partners listed in thepartner-priority-order cookie 220. In such some examples, blocks 1110,1114, 1116, 1118, and 1120 are omitted and at block 1112, the impressionmonitor system 132 sends the client computer 202, 203 an acknowledgementresponse without sending a next partner redirect.

Turning now to FIG. 12, the example flow diagram may be executed todynamically designate preferred web service providers (or preferredpartners) from which to request logging of impressions using the exampleredirection beacon request processes of FIGS. 10 and 11. The exampleprocess of FIG. 12 is described in connection with the example system200 of FIG. 2. Initial impressions associated with content and/or adsdelivered by a particular publisher site (e.g., the publisher 302 ofFIG. 3) trigger the beacon instructions 214 (FIG. 2) (and/or beaconinstructions at other computers) to request logging of impressions at apreferred partner (block 1202). In this illustrated example, thepreferred partner is initially the partner A site 206 (FIGS. 2 and 3).The impression monitor system 132 (FIGS. 1, 2, and 3) receives feedbackon non-matching user IDs from the preferred partner 206 (block 1204).The rules/ML engine 230 (FIG. 2) updates the preferred partner for thenon-matching user IDs (block 1206) based on the feedback received atblock 1204. In some examples, during the operation of block 1206, theimpression monitor system 132 also updates a partner-priority-order ofpreferred partners in the partner-priority-order cookie 220 of FIG. 2.Subsequent impressions trigger the beacon instructions 214 (and/orbeacon instructions at other computers 202, 203) to send requests forlogging of impressions to different respective preferred partnersspecifically based on each user ID (block 1208). That is, some user IDsin the panelist monitor cookie 218 and/or the partner cookie(s) 216 maybe associated with one preferred partner, while others of the user IDsare now associated with a different preferred partner as a result of theoperation at block 1206. The example process of FIG. 12 then ends.

FIG. 13 depicts an example system 1300 that may be used to determinemedia (e.g., content and/or advertising) exposure based on informationcollected by one or more database proprietors. The example system 1300is another example of the systems 200 and 300 illustrated in FIGS. 2 and3 in which an intermediary 1308, 1312 is provided between a clientcomputer 1304 and a partner 1310, 1314. Persons of ordinary skill in theart will understand that the description of FIGS. 2 and 3 and thecorresponding flow diagrams of FIGS. 8-12 are applicable to the system1300 with the inclusion of the intermediary 1308, 1312.

According to the illustrated example, a publisher 1302 transmits anadvertisement or other media content to the client computer 1304. Thepublisher 1302 may be the publisher 302 described in conjunction withFIG. 3. The client computer 1304 may be the panelist client computer 202or the non-panelist computer 203 described in conjunction with FIGS. 2and 3 or any other client computer. The advertisement or other mediacontent includes a beacon that instructs the client computer to send arequest to an impression monitor system 1306 as explained above.

The impression monitor system 1306 may be the impression monitor system132 described in conjunction with FIGS. 1-3. The impression monitorsystem 1306 of the illustrated example receives beacon requests from theclient computer 1304 and transmits redirection messages to the clientcomputer 1304 to instruct the client to send a request to one or more ofthe intermediary A 1308, the intermediary B 1312, or any other systemsuch as another intermediary, a partner, etc. The impression monitorsystem 1306 also receives information about partner cookies from one ormore of the intermediary A 1308 and the intermediary B 1312.

In some examples, the impression monitor system 1306 may insert into aredirection message an identifier of a client that is established by theimpression monitor system 1306 and identifies the client computer 1304and/or a user thereof. For example, the identifier of the client may bean identifier stored in a cookie that has been set at the client by theimpression monitor system 1306 or any other entity, an identifierassigned by the impression monitor system 1306 or any other entity, etc.The identifier of the client may be a unique identifier, a semi-uniqueidentifier, etc. In some examples, the identifier of the client may beencrypted, obfuscated, or varied to prevent tracking of the identifierby the intermediary 1308, 1312 or the partner 1310, 1314. According tothe illustrated example, the identifier of the client is included in theredirection message to the client computer 1304 to cause the clientcomputer 1304 to transmit the identifier of the client to theintermediary 1308, 1312 when the client computer 1304 follows theredirection message. For example, the identifier of the client may beincluded in a URL included in the redirection message to cause theclient computer 1304 to transmit the identifier of the client to theintermediary 1308, 1312 as a parameter of the request that is sent inresponse to the redirection message.

The intermediaries 1308, 1312 of the illustrated example receiveredirected beacon requests from the client computer 1304 and transmitinformation about the requests to the partners 1310, 1314. The exampleintermediaries 1308, 1312 are made available on a content deliverynetwork (e.g., one or more servers of a content delivery network) toensure that clients can quickly send the requests without causingsubstantial interruption in the access of content from the publisher1302.

In examples disclosed herein, a cookie set in a domain (e.g.,“partnerA.com”) is accessible by a server of a sub-domain (e.g.,“intermediary.partnerA.com”) corresponding to the domain (e.g., the rootdomain “partnerA.com”) in which the cookie was set. In some examples,the reverse is also true such that a cookie set in a sub-domain (e.g.,“intermediary.partnerA.com”) is accessible by a server of a root domain(e.g., the root domain “partnerA.com”) corresponding to the sub-domain(e.g., “intermediary.partnerA.com”) in which the cookie was set. As usedherein, the term domain (e.g., Internet domain, domain name, etc.)includes the root domain (e.g., “domain.com”) and sub-domains (e.g.,“a.domain.com,” “b.domain.com,” “c.d.domain.com,” etc.).

To enable the example intermediaries 1308, 1312 to receive cookieinformation associated with the partners 1310, 1314 respectively,sub-domains of the partners 1310, 1314 are assigned to theintermediaries 1308, 1312. For example, the partner A 1310 may registeran internet address associated with the intermediary A 1308 with thesub-domain in a domain name system associated with a domain for thepartner A 1310. Alternatively, the sub-domain may be associated with theintermediary in any other manner. In such examples, cookies set for thedomain name of partner A 1310 are transmitted from the client computer1304 to the intermediary A 1308 that has been assigned a sub-domain nameassociated with the domain of partner A 1310 when the client 1304transmits a request to the intermediary A 1308.

The example intermediaries 1308, 1312 transmit the beacon requestinformation including a campaign ID and received cookie information tothe partners 1310, 1314 respectively. This information may be stored atthe intermediaries 1308, 1312 so that it can be sent to the partners1310, 1314 in a batch. For example, the received information could betransmitted near the end of the day, near the end of the week, after athreshold amount of information is received, etc. Alternatively, theinformation may be transmitted immediately upon receipt. The campaign IDmay be encrypted, obfuscated, varied, etc. to prevent the partners 1310,1314 from recognizing the content to which the campaign ID correspondsor to otherwise protect the identity of the content. A lookup table ofcampaign ID information may be stored at the impression monitor system1306 so that impression information received from the partners 1310,1314 can be correlated with the content.

The intermediaries 1308, 1312 of the illustrated example also transmitan indication of the availability of a partner cookie to the impressionmonitor system 1306. For example, when a redirected beacon request isreceived at the intermediary A 1308, the intermediary A 1308 determinesif the redirected beacon request includes a cookie for partner A 1310.The intermediary A 1308 sends the notification to the impression monitorsystem 1306 when the cookie for partner A 1310 was received.Alternatively, intermediaries 1308, 1312 may transmit information aboutthe availability of the partner cookie regardless of whether a cookie isreceived. Where the impression monitor system 1306 has included anidentifier of the client in the redirection message and the identifierof the client is received at the intermediaries 1308, 1312, theintermediaries 1308, 1312 may include the identifier of the client withthe information about the partner cookie transmitted to the impressionmonitor system 1306. The impression monitor system 1306 may use theinformation about the existence of a partner cookie to determine how toredirect future beacon requests. For example, the impression monitorsystem 1306 may elect not to redirect a client to an intermediary 1308,1312 that is associated with a partner 1310, 1314 with which it has beendetermined that a client does not have a cookie. In some examples, theinformation about whether a particular client has a cookie associatedwith a partner may be refreshed periodically to account for cookiesexpiring and new cookies being set (e.g., a recent login or registrationat one of the partners).

The intermediaries 1308, 1312 may be implemented by a server associatedwith a content metering entity (e.g., a content metering entity thatprovides the impression monitor system 1306). Alternatively,intermediaries 1308, 1312 may be implemented by servers associated withthe partners 1310, 1314 respectively. In other examples, theintermediaries may be provided by a third-party such as a contentdelivery network.

In some examples, the intermediaries 1308, 1312 are provided to preventa direct connection between the partners 1310, 1314 and the clientcomputer 1304, to prevent some information from the redirected beaconrequest from being transmitted to the partners 1310, 1314 (e.g., toprevent a REFERRER_URL from being transmitted to the partners 1310,1314), to reduce the amount of network traffic at the partners 1310,1314 associated with redirected beacon requests, and/or to transmit tothe impression monitor system 1306 real-time or near real-timeindications of whether a partner cookie is provided by the clientcomputer 1304.

In some examples, the intermediaries 1308, 1312 are trusted by thepartners 1310, 1314 to prevent confidential data from being transmittedto the impression monitor system 1306. For example, the intermediary1308, 1312 may remove identifiers stored in partner cookies beforetransmitting information to the impression monitor system 1306.

The partners 1310, 1314 receive beacon request information including thecampaign ID and cookie information from the intermediaries 1308, 1312.The partners 1310, 1314 determine identity and demographics for a userof the client computer 1304 based on the cookie information. The examplepartners 1310, 1314 track impressions for the campaign ID based on thedetermined demographics associated with the impression. Based on thetracked impressions, the example partners 1310, 1314 generate reports(previously described). The reports may be sent to the impressionmonitor system 1306, the publisher 1302, an advertiser that supplied anad provided by the publisher 1302, a media content hub, or other personsor entities interested in the reports.

FIG. 14 is a flow diagram representative of example machine readableinstructions that may be executed to process a redirected request at anintermediary. The example process of FIG. 14 is described in connectionwith the example intermediary A 1308. Some or all of the blocks mayadditionally or alternatively be performed by one or more of the exampleintermediary B 1312, the partners 1310, 1314 of FIG. 13 or by otherpartners described in conjunction with FIGS. 1-3.

According to the illustrated example, intermediary A 1308 receives aredirected beacon request from the client computer 1304 (block 1402).The intermediary A 1308 determines if the client computer 1304transmitted a cookie associated with partner A 1310 in the redirectedbeacon request (block 1404). For example, when the intermediary A 1308is assigned a domain name that is a sub-domain of partner A 1310, theclient computer 1304 will transmit a cookie set by partner A 1310 to theintermediary A 1308.

When the redirected beacon request does not include a cookie associatedwith partner A 1310 (block 1404), control proceeds to block 1412 whichis described below. When the redirected beacon request includes a cookieassociated with partner A 1310 (block 1404), the intermediary A 1308notifies the impression monitor system 1306 of the existence of thecookie (block 1406). The notification may additionally includeinformation associated with the redirected beacon request (e.g., asource URL, a campaign ID, etc.), an identifier of the client, etc.According to the illustrated example, the intermediary A 1308 stores acampaign ID included in the redirected beacon request and the partnercookie information (block 1408). The intermediary A 1308 mayadditionally store other information associated with the redirectedbeacon request such as, for example, a source URL, a referrer URL, etc.

The example intermediary A 1308 then determines if stored informationshould be transmitted to the partner A 1310 (block 1410). For example,the intermediary A 1308 may determine that information should betransmitted immediately, may determine that a threshold amount ofinformation has been received, may determine that the information shouldbe transmitted based on the time of day, etc. When the intermediary A1308 determines that the information should not be transmitted (block1410), control proceeds to block 1412. When the intermediary A 1308determines that the information should be transmitted (block 1410), theintermediary A 1308 transmits stored information to the partner A 1310.The stored information may include information associated with a singlerequest, information associated with multiple requests from a singleclient, information associated with multiple requests from multipleclients, etc.

According to the illustrated example, the intermediary A 1308 thendetermines if a next intermediary and/or partner should be contacted bythe client computer 1304 (block 1412). The example intermediary A 1308determines that the next partner should be contacted when a cookieassociated with partner a 1310 is not received. Alternatively, theintermediary A 1308 may determine that the next partner should becontacted whenever a redirected beacon request is received, associatedwith the partner cookie, etc.

When the intermediary A 1308 determines that the next partner (e.g.,intermediary B 1314) should be contacted (block 1412), the intermediaryA 1308 transmits a beacon redirection message to the client computer1304 indicating that the client computer 1304 should send a request tothe intermediary B 1312. After transmitting the redirection message(block 1414) or when the intermediary A 1308 determines that the nextpartner should not be contacted (block 1412), the example process ofFIG. 14 ends.

While the example of FIG. 14 describes an approach where eachintermediary 1308, 1312 selectively or automatically transmits aredirection message identifying the next intermediary 1308, 1312 in achain, other approaches may be implemented. For example, the redirectionmessage from the impression monitor system 1306 may identify multipleintermediaries 1308, 1312. In such an example, the redirection messagemay instruct the client computer 1304 to send a request to each of theintermediaries 1308, 1312 (or a subset) sequentially, may instruct theclient computer 1304 to send requests to each of the intermediaries1308, 1312 in parallel (e.g., using JavaScript instructions that supportrequests executed in parallel), etc.

While the example of FIG. 14 is described in conjunction withintermediary A, some or all of the blocks of FIG. 14 may be performed bythe intermediary B 1312, one or more of the partners 1310, 1314, anyother partner described herein, or any other entity or system.Additionally or alternatively, multiple instances of FIG. 14 (or anyother instructions described herein) may be performed in parallel at anynumber of locations.

FIG. 15 is a block diagram of an example processor system 1510 that maybe used to implement the example apparatus, methods, articles ofmanufacture, and/or systems disclosed herein. As shown in FIG. 15, theprocessor system 1510 includes a processor 1512 that is coupled to aninterconnection bus 1514. The processor 1512 may be any suitableprocessor, processing unit, or microprocessor. Although not shown inFIG. 15, the system 1510 may be a multi-processor system and, thus, mayinclude one or more additional processors that are identical or similarto the processor 1512 and that are communicatively coupled to theinterconnection bus 1514.

The processor 1512 of FIG. 15 is coupled to a chipset 1518, whichincludes a memory controller 1520 and an input/output (I/O) controller1522. A chipset provides I/O and memory management functions as well asa plurality of general purpose and/or special purpose registers, timers,etc. that are accessible or used by one or more processors coupled tothe chipset 1518. The memory controller 1520 performs functions thatenable the processor 1512 (or processors if there are multipleprocessors) to access a system memory 1524, a mass storage memory 1525,and/or an optical media 1527.

In general, the system memory 1524 may include any desired type ofvolatile and/or non-volatile memory such as, for example, static randomaccess memory (SRAM), dynamic random access memory (DRAM), flash memory,read-only memory (ROM), etc. The mass storage memory 1525 may includeany desired type of mass storage device including hard disk drives,optical drives, tape storage devices, etc. The optical media 1527 mayinclude any desired type of optical media such as a digital versatiledisc (DVD), a compact disc (CD), or a blu-ray optical disc. Theinstructions of any of FIGS. 9-12 and 14 may be stored on any of thetangible media represented by the system memory 1524, the mass storagedevice 1525, and/or any other media.

The I/O controller 1522 performs functions that enable the processor1512 to communicate with peripheral input/output (I/O) devices 1526 and1528 and a network interface 1530 via an I/O bus 1532. The I/O devices1526 and 1528 may be any desired type of I/O device such as, forexample, a keyboard, a video display or monitor, a mouse, etc. Thenetwork interface 1530 may be, for example, an Ethernet device, anasynchronous transfer mode (ATM) device, an 802.11 device, a digitalsubscriber line (DSL) modem, a cable modem, a cellular modem, etc. thatenables the processor system 1310 to communicate with another processorsystem.

While the memory controller 1520 and the I/O controller 1522 aredepicted in FIG. 15 as separate functional blocks within the chipset1518, the functions performed by these blocks may be integrated within asingle semiconductor circuit or may be implemented using two or moreseparate integrated circuits.

Although the foregoing discloses the use of cookies for transmittingidentification information from clients to servers, any other system fortransmitting identification information from clients to servers or othercomputers may be used. For example, identification information or anyother information provided by any of the cookies disclosed herein may beprovided by an Adobe Flash® client identifier, identificationinformation stored in an HTMLS datastore, etc. The methods and apparatusdescribed herein are not limited to implementations that employ cookies.

Although certain methods, apparatus, systems, and articles ofmanufacture have been disclosed herein, the scope of coverage of thispatent is not limited thereto. To the contrary, this patent covers allmethods, apparatus, systems, and articles of manufacture fairly fallingwithin the scope of the claims either literally or under the doctrine ofequivalents.

What is claimed is:
 1. A method to monitor media exposure, the methodcomprising: receiving a first request from a client device at anintermediary serving a first sub-domain of a first internet domain, thefirst request to be indicative of access to media at the client device;transmitting, from the intermediary, first data associated with thefirst request to a data collection server of an audience measuremententity and second data about the first request to an entity of the firstinternet domain; and in response to determining that the first requestdoes not include a user identifier associated with the first internetdomain, sending a first response from the intermediary to the clientdevice, the first response to instruct the client device to send asecond request to a second intermediary associated with a secondsub-domain of a second internet domain, the second request indicative ofthe access to the media at the client device.
 2. The method as definedin claim 1, wherein the user identifier is a cookie.
 3. The method asdefined in claim 1, further including transmitting, from the secondintermediary, third data associated with the second request to the datacollection server of the audience measurement entity and fourth dataassociated with the second request to an entity of the second internetdomain.
 4. The method as defined in claim 3, wherein the third dataincludes a status of a second user identifier associated with the entityof the second internet domain.
 5. The method as defined in claim 3,wherein the fourth data includes an identifier corresponding to themedia, and a second user identifier associated with the second internetdomain.
 6. The method as defined in claim 5, wherein the identifier is acampaign identifier to identify an advertisement in the media.
 7. Themethod as defined in claim 5, wherein the identifier is encrypted toprevent the entity of the second internet domain from recognizing theidentifier.
 8. A system to monitor media exposure, the systemcomprising: an intermediary to: receive a first request from a clientdevice at an intermediary serving a first sub-domain of a first internetdomain, the first request to be indicative of access to media at theclient device; transmit, from the intermediary, first data associatedwith the first request to a data collection server of an audiencemeasurement entity and second data about the first request to an entityof the first internet domain; and in response to determining that thefirst request does not include a user identifier associated with thefirst internet domain, send a first response from the intermediary tothe client device, the first response to instruct the client device tosend a second request to a second intermediary associated with a secondsub-domain of a second internet domain, the second request indicative ofthe access to the media at the client device.
 9. The system as definedin claim 8, wherein the user identifier is a cookie.
 10. The system asdefined in claim 8, wherein the second intermediary is to transmit thirddata associated with the second request to the data collection server ofthe audience measurement entity and fourth data associated with thesecond request to an entity of the second internet domain.
 11. Thesystem as defined in claim 10, wherein the third data includes a statusof a second user identifier associated with the entity of the secondinternet domain.
 12. The system as defined in claim 10, wherein thefourth data includes an identifier corresponding to the media, and asecond user identifier associated with the second internet domain. 13.The system as defined in claim 12, wherein the identifier is a campaignidentifier to identify an advertisement in the media.
 14. The system asdefined in claim 12, wherein the identifier is encrypted to prevent theentity of the second internet domain from recognizing the identifier.15. A tangible computer readable storage disc or storage devicecomprising instructions that, when executed, cause a processor to atleast: receive a first request from a client device at an intermediaryserving a first sub-domain of a first internet domain, the first requestto be indicative of access to media at the client device; transmit, fromthe intermediary, first data associated with the first request to a datacollection server of an audience measurement entity and second dataabout the first request to an entity of the first internet domain; andin response to determining that the first request does not include auser identifier associated with the first internet domain, send a firstresponse from the intermediary to the client device, the first responseto instruct the client device to send a second request to a secondintermediary associated with a second sub-domain of a second internetdomain, the second request indicative of the access to the media at theclient device.
 16. The tangible computer readable storage disc orstorage device as defined in claim 15, wherein the user identifier is acookie.
 17. The tangible computer readable storage disc or storagedevice as defined in claim 15, where the instructions, when executed,cause the processor to transmit, from the second intermediary, thirddata associated with the second request to the data collection server ofthe audience measurement entity and fourth data associated with thesecond request to an entity of the second internet domain.
 18. Thetangible computer readable storage disc or storage device as defined inclaim 17, wherein the third data includes a status of a second useridentifier associated with the entity of the second internet domain. 19.The tangible computer readable storage disc or storage device as definedin claim 17, wherein the fourth data includes an identifiercorresponding to the media, and a second user identifier associated withthe second internet domain.
 20. The tangible computer readable storagedisc or storage device as defined in claim 19, wherein the identifier isa campaign identifier to identify an advertisement in the media.
 21. Thetangible computer readable storage disc or storage device as defined inclaim 19, wherein the identifier is encrypted to prevent the entity ofthe second internet domain from recognizing the identifier.